{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xCLcAmON-m2i",
    "colab_type": "text"
   },
   "source": [
    "# Tensor2Tensor Reinforcement Learning\n",
    "\n",
    "The `rl` package provides the ability to run model-free and model-based reinforcement learning algorithms.\n",
    "\n",
    "Currently, we support the Proximal Policy Optimization ([PPO](https://arxiv.org/abs/1707.06347)) and Simulated Policy Learning ([SimPLe](https://arxiv.org/abs/1903.00374)).\n",
    "\n",
    "Below you will find examples of PPO training using `trainer_model_free.py` and SimPLe traning using `trainer_model_based.py`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "RW7gEGp3e87G",
    "colab_type": "code",
    "colab": {},
    "cellView": "form"
   },
   "outputs": [],
   "source": [
    "#@title\n",
    "# Copyright 2018 Google LLC.\n",
    "\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "\n",
    "# https://www.apache.org/licenses/LICENSE-2.0\n",
    "\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "pq0BqXm4-3gJ",
    "colab_type": "code",
    "outputId": "6086719f-6268-4b61-8fa3-d251eda24c97",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1.553273826475E12,
     "user_tz": -60.0,
     "elapsed": 20650.0,
     "user": {
      "displayName": "Piotr Miłoś",
      "photoUrl": "https://lh3.googleusercontent.com/-050ZBEGpNAA/AAAAAAAAAAI/AAAAAAAAk9g/r6cv_J6J5qA/s64/photo.jpg",
      "userId": "12158759908531801397"
     }
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 163.0
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[K    100% |████████████████████████████████| 1.3MB 9.4MB/s \n",
      "\u001b[K    100% |████████████████████████████████| 215kB 27.3MB/s \n",
      "\u001b[K    100% |████████████████████████████████| 143kB 29.6MB/s \n",
      "\u001b[K    100% |████████████████████████████████| 21.1MB 1.7MB/s \n",
      "\u001b[K    100% |████████████████████████████████| 409kB 24.7MB/s \n",
      "\u001b[K    100% |████████████████████████████████| 296kB 25.0MB/s \n",
      "\u001b[K    100% |████████████████████████████████| 61kB 21.5MB/s \n",
      "\u001b[?25h  Building wheel for pypng (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Building wheel for opt-einsum (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h"
     ]
    }
   ],
   "source": [
    "!pip install -q tensorflow==1.13.1\n",
    "!pip install -q tensorflow_probability==0.6.0\n",
    "!pip install -q tensor2tensor==1.13.1\n",
    "!pip install -q gym[atari]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "R7-Ni-39DGZW",
    "colab_type": "code",
    "colab": {}
   },
   "outputs": [],
   "source": [
    "# Helper function for playing videos in the colab.\n",
    "def play_video(path):\n",
    "  from IPython.core.magics.display import HTML\n",
    "  display_path = \"/nbextensions/vid.mp4\"\n",
    "  display_abs_path = \"/usr/local/share/jupyter\" + display_path\n",
    "  !rm -f $display_abs_path\n",
    "  !ffmpeg -loglevel error -i $path $display_abs_path\n",
    "  return HTML(\"\"\"\n",
    "    <video width=\"640\" height=\"480\" controls>\n",
    "      <source src=\"{}\" type=\"video/mp4\">\n",
    "    </video>\n",
    "  \"\"\".format(display_path))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pueuiKUmAOUT",
    "colab_type": "text"
   },
   "source": [
    "# Play using a pre-trained policy\n",
    "\n",
    "We provide pretrained policies for the following games from the Atari Learning Environment ( [ALE](https://github.com/mgbellemare/Arcade-Learning-Environment)) : alien,\n",
    "amidar,\n",
    " assault,\n",
    " asterix,\n",
    " asteroids,\n",
    " atlantis,\n",
    " bank_heist,\n",
    " battle_zone,\n",
    " beam_rider,\n",
    " bowling,\n",
    " boxing,\n",
    " breakout,\n",
    " chopper_command,\n",
    " crazy_climber,\n",
    " demon_attack,\n",
    " fishing_derby,\n",
    " freeway,\n",
    " frostbite,\n",
    " gopher,\n",
    " gravitar,\n",
    " hero,\n",
    " ice_hockey,\n",
    " jamesbond,\n",
    " kangaroo,\n",
    " krull,\n",
    " kung_fu_master,\n",
    " ms_pacman,\n",
    " name_this_game,\n",
    " pong,\n",
    " private_eye,\n",
    " qbert,\n",
    " riverraid,\n",
    " road_runner,\n",
    " seaquest,\n",
    " up_n_down,\n",
    " yars_revenge.\n",
    " \n",
    " We have 5 checkpoints for each game saved on Google Storage. Run the following command get the storage path:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "x9pKfNbDFfVh",
    "colab_type": "code",
    "outputId": "97e763cc-caaa-49c8-e532-fcbde828d1a2",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1.5532741511E12,
     "user_tz": -60.0,
     "elapsed": 6162.0,
     "user": {
      "displayName": "Piotr Miłoś",
      "photoUrl": "https://lh3.googleusercontent.com/-050ZBEGpNAA/AAAAAAAAAAI/AAAAAAAAk9g/r6cv_J6J5qA/s64/photo.jpg",
      "userId": "12158759908531801397"
     }
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 147.0
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
      "For more information, please see:\n",
      "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
      "  * https://github.com/tensorflow/addons\n",
      "If you depend on functionality not listed there, please file an issue.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'gs://tensor2tensor-checkpoints/modelrl_experiments/train_sd/143'"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# experiment_id is an integer from [0, 4].\n",
    "def get_run_dir(game, experiment_id):\n",
    "  from tensor2tensor.data_generators.gym_env import ATARI_GAMES_WITH_HUMAN_SCORE_NICE\n",
    "  EXPERIMENTS_PER_GAME = 5\n",
    "  run_id = ATARI_GAMES_WITH_HUMAN_SCORE_NICE.index(game) * EXPERIMENTS_PER_GAME + experiment_id + 1\n",
    "  return \"gs://tensor2tensor-checkpoints/modelrl_experiments/train_sd/{}\".format(run_id)\n",
    "\n",
    "get_run_dir('pong', 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "77fFdm-cFEOB",
    "colab_type": "text"
   },
   "source": [
    "To evaluate and generate videos for a pretrained policy on Pong:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "X-nGlbuTAQXj",
    "colab_type": "code",
    "outputId": "888968f2-f551-4a0f-9fc7-074a949362d6",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1.553271580737E12,
     "user_tz": -60.0,
     "elapsed": 842128.0,
     "user": {
      "displayName": "Piotr Kozakowski",
      "photoUrl": "",
      "userId": "01014928596539690143"
     }
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 17088.0
    },
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
      "For more information, please see:\n",
      "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
      "  * https://github.com/tensorflow/addons\n",
      "If you depend on functionality not listed there, please file an issue.\n",
      "\n",
      "INFO:tensorflow:Overriding hparams in rlmb_long_stochastic_discrete with game=pong,eval_max_num_noops=8,eval_sampling_temps=[0.5]\n",
      "INFO:tensorflow:Evaluating metric mean_reward/eval/sampling_temp_0.5_max_noops_8_unclipped\n",
      "2019-03-22 16:05:45.007030: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2300000000 Hz\n",
      "2019-03-22 16:05:45.007306: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x2697860 executing computations on platform Host. Devices:\n",
      "2019-03-22 16:05:45.007346: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): <undefined>, <undefined>\n",
      "2019-03-22 16:05:45.105281: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2019-03-22 16:05:45.105857: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x2697440 executing computations on platform CUDA. Devices:\n",
      "2019-03-22 16:05:45.105908: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): Tesla K80, Compute Capability 3.7\n",
      "2019-03-22 16:05:45.106380: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties: \n",
      "name: Tesla K80 major: 3 minor: 7 memoryClockRate(GHz): 0.8235\n",
      "pciBusID: 0000:00:04.0\n",
      "totalMemory: 11.17GiB freeMemory: 11.10GiB\n",
      "2019-03-22 16:05:45.106420: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0\n",
      "2019-03-22 16:05:45.499212: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
      "2019-03-22 16:05:45.499307: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0 \n",
      "2019-03-22 16:05:45.499332: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N \n",
      "2019-03-22 16:05:45.499671: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:42] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
      "2019-03-22 16:05:45.499741: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10754 MB memory) -> physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7)\n",
      "INFO:tensorflow:Using DummyPolicyProblem for the policy.\n",
      "INFO:tensorflow:Setting T2TModel mode to 'train'\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "INFO:tensorflow:Using variable initializer: orthogonal\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensor2tensor/utils/t2t_model.py:1358: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "INFO:tensorflow:Transforming feature 'input_action' with symbol_modality_6_64.bottom\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/function.py:1007: calling Graph.create_op (from tensorflow.python.framework.ops) with compute_shapes is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Shapes are always computed; don't use the compute_shapes as it has no effect.\n",
      "INFO:tensorflow:Transforming feature 'input_reward' with symbol_modality_3_64.bottom\n",
      "INFO:tensorflow:Transforming feature 'inputs' with video_modality.bottom\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensor2tensor/layers/common_video.py:495: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "tf.py_func is deprecated in TF V2. Instead, use\n",
      "    tf.py_function, which takes a python function which manipulates tf eager\n",
      "    tensors instead of numpy arrays. It's easy to convert a tf eager tensor to\n",
      "    an ndarray (just call tensor.numpy()) but having access to eager tensors\n",
      "    means `tf.py_function`s can use accelerators such as GPUs as well as\n",
      "    being differentiable using a gradient tape.\n",
      "    \n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensor2tensor/layers/common_layers.py:277: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "INFO:tensorflow:Transforming feature 'target_action' with symbol_modality_6_64.targets_bottom\n",
      "INFO:tensorflow:Transforming feature 'target_policy' with identity_modality.targets_bottom\n",
      "INFO:tensorflow:Transforming feature 'target_reward' with symbol_modality_3_64.targets_bottom\n",
      "INFO:tensorflow:Transforming feature 'target_value' with identity_modality.targets_bottom\n",
      "INFO:tensorflow:Transforming feature 'targets' with video_modality.targets_bottom\n",
      "INFO:tensorflow:Building model body\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensor2tensor/models/research/rl.py:598: conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.conv2d instead.\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensor2tensor/models/research/rl.py:602: flatten (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.flatten instead.\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensor2tensor/models/research/rl.py:603: dropout (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.dropout instead.\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensor2tensor/models/research/rl.py:604: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.dense instead.\n",
      "INFO:tensorflow:Transforming body output with identity_modality.top\n",
      "INFO:tensorflow:Transforming body output with identity_modality.top\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensor2tensor/layers/common_layers.py:2887: multinomial (from tensorflow.python.ops.random_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.random.categorical instead.\n",
      "2019-03-22 16:06:00.352605: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0\n",
      "2019-03-22 16:06:00.352688: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
      "2019-03-22 16:06:00.352724: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0 \n",
      "2019-03-22 16:06:00.352744: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N \n",
      "2019-03-22 16:06:00.353037: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10754 MB memory) -> physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7)\n",
      "2019-03-22 16:06:00.588787: W tensorflow/core/platform/cloud/google_auth_provider.cc:178] All attempts to get a Google authentication bearer token failed, returning an empty token. Retrieving token from files failed with \"Not found: Could not locate the credentials file.\". Retrieving token from GCE failed with \"Cancelled: GCE check skipped due to presence of $NO_GCE_CHECK environment variable.\".\n",
      "2019-03-22 16:06:00.647797: W tensorflow/core/platform/cloud/google_auth_provider.cc:178] All attempts to get a Google authentication bearer token failed, returning an empty token. Retrieving token from files failed with \"Not found: Could not locate the credentials file.\". Retrieving token from GCE failed with \"Cancelled: GCE check skipped due to presence of $NO_GCE_CHECK environment variable.\".\n",
      "INFO:tensorflow:Restoring checkpoint gs://tensor2tensor-checkpoints/modelrl_experiments/train_sd/142/policy/model.ckpt-171992\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/saver.py:1266: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use standard file APIs to check for files with this prefix.\n",
      "2019-03-22 16:06:00.711910: W tensorflow/core/platform/cloud/google_auth_provider.cc:178] All attempts to get a Google authentication bearer token failed, returning an empty token. Retrieving token from files failed with \"Not found: Could not locate the credentials file.\". Retrieving token from GCE failed with \"Cancelled: GCE check skipped due to presence of $NO_GCE_CHECK environment variable.\".\n",
      "INFO:tensorflow:Restoring parameters from gs://tensor2tensor-checkpoints/modelrl_experiments/train_sd/142/policy/model.ckpt-171992\n",
      "2019-03-22 16:06:00.793701: W tensorflow/core/platform/cloud/google_auth_provider.cc:178] All attempts to get a Google authentication bearer token failed, returning an empty token. Retrieving token from files failed with \"Not found: Could not locate the credentials file.\". Retrieving token from GCE failed with \"Cancelled: GCE check skipped due to presence of $NO_GCE_CHECK environment variable.\".\n",
      "2019-03-22 16:06:00.953239: W tensorflow/core/platform/cloud/google_auth_provider.cc:178] All attempts to get a Google authentication bearer token failed, returning an empty token. Retrieving token from files failed with \"Not found: Could not locate the credentials file.\". Retrieving token from GCE failed with \"Cancelled: GCE check skipped due to presence of $NO_GCE_CHECK environment variable.\".\n",
      "2019-03-22 16:06:01.086594: W tensorflow/core/platform/cloud/google_auth_provider.cc:178] All attempts to get a Google authentication bearer token failed, returning an empty token. Retrieving token from files failed with \"Not found: Could not locate the credentials file.\". Retrieving token from GCE failed with \"Cancelled: GCE check skipped due to presence of $NO_GCE_CHECK environment variable.\".\n",
      "2019-03-22 16:06:01.259521: W tensorflow/core/platform/cloud/google_auth_provider.cc:178] All attempts to get a Google authentication bearer token failed, returning an empty token. Retrieving token from files failed with \"Not found: Could not locate the credentials file.\". Retrieving token from GCE failed with \"Cancelled: GCE check skipped due to presence of $NO_GCE_CHECK environment variable.\".\n",
      "2019-03-22 16:06:01.322896: W tensorflow/core/platform/cloud/google_auth_provider.cc:178] All attempts to get a Google authentication bearer token failed, returning an empty token. Retrieving token from files failed with \"Not found: Could not locate the credentials file.\". Retrieving token from GCE failed with \"Cancelled: GCE check skipped due to presence of $NO_GCE_CHECK environment variable.\".\n",
      "2019-03-22 16:06:03.034751: I tensorflow/stream_executor/dso_loader.cc:152] successfully opened CUDA library libcublas.so.10.0 locally\n",
      "INFO:tensorflow:Step 5, mean_score: 0.000000\n",
      "INFO:tensorflow:Step 10, mean_score: 0.000000\n",
      "INFO:tensorflow:Step 15, mean_score: 0.000000\n",
      "INFO:tensorflow:Step 20, mean_score: 0.000000\n",
      "INFO:tensorflow:Step 25, mean_score: 0.000000\n",
      "INFO:tensorflow:Step 30, mean_score: 0.000000\n",
      "INFO:tensorflow:Step 35, mean_score: 0.000000\n",
      "INFO:tensorflow:Step 40, mean_score: 0.000000\n",
      "INFO:tensorflow:Step 45, mean_score: 0.000000\n",
      "INFO:tensorflow:Step 50, mean_score: 0.000000\n",
      "INFO:tensorflow:Step 55, mean_score: 0.000000\n",
      "INFO:tensorflow:Step 60, mean_score: -0.015625\n",
      "INFO:tensorflow:Step 65, mean_score: -0.078125\n",
      "INFO:tensorflow:Step 70, mean_score: -0.078125\n",
      "INFO:tensorflow:Step 75, mean_score: -0.078125\n",
      "INFO:tensorflow:Step 80, mean_score: -0.078125\n",
      "INFO:tensorflow:Step 85, mean_score: -0.078125\n",
      "INFO:tensorflow:Step 90, mean_score: 0.484375\n",
      "INFO:tensorflow:Step 95, mean_score: 0.843750\n",
      "INFO:tensorflow:Step 100, mean_score: 0.828125\n",
      "INFO:tensorflow:Step 105, mean_score: 0.828125\n",
      "INFO:tensorflow:Step 110, mean_score: 0.828125\n",
      "INFO:tensorflow:Step 115, mean_score: 0.828125\n",
      "INFO:tensorflow:Step 120, mean_score: 0.828125\n",
      "INFO:tensorflow:Step 125, mean_score: 0.828125\n",
      "INFO:tensorflow:Step 130, mean_score: 0.812500\n",
      "INFO:tensorflow:Step 135, mean_score: 0.812500\n",
      "INFO:tensorflow:Step 140, mean_score: 0.812500\n",
      "INFO:tensorflow:Step 145, mean_score: 0.812500\n",
      "INFO:tensorflow:Step 150, mean_score: 0.812500\n",
      "INFO:tensorflow:Step 155, mean_score: 0.812500\n",
      "INFO:tensorflow:Step 160, mean_score: 0.812500\n",
      "INFO:tensorflow:Step 165, mean_score: 0.812500\n",
      "INFO:tensorflow:Step 170, mean_score: 0.828125\n",
      "INFO:tensorflow:Step 175, mean_score: 0.843750\n",
      "INFO:tensorflow:Step 180, mean_score: 0.843750\n",
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      "INFO:tensorflow:Step 1860, mean_score: 19.671875\n",
      "INFO:tensorflow:Step 1865, mean_score: 19.671875\n",
      "INFO:tensorflow:Step 1870, mean_score: 19.687500\n",
      "INFO:tensorflow:Step 1875, mean_score: 19.687500\n",
      "INFO:tensorflow:Step 1880, mean_score: 19.703125\n",
      "INFO:tensorflow:Step 1885, mean_score: 19.703125\n",
      "INFO:tensorflow:Step 1890, mean_score: 19.703125\n",
      "INFO:tensorflow:Step 1895, mean_score: 19.718750\n",
      "INFO:tensorflow:Step 1900, mean_score: 19.734375\n",
      "INFO:tensorflow:Step 1905, mean_score: 19.734375\n",
      "INFO:tensorflow:Step 1910, mean_score: 19.734375\n",
      "INFO:tensorflow:Step 1915, mean_score: 19.734375\n",
      "INFO:tensorflow:Step 1920, mean_score: 19.734375\n",
      "INFO:tensorflow:Step 1925, mean_score: 19.734375\n",
      "INFO:tensorflow:Step 1930, mean_score: 19.750000\n",
      "INFO:tensorflow:Step 1935, mean_score: 19.750000\n",
      "INFO:tensorflow:Step 1940, mean_score: 19.750000\n",
      "INFO:tensorflow:Step 1945, mean_score: 19.750000\n",
      "INFO:tensorflow:Step 1950, mean_score: 19.765625\n",
      "INFO:tensorflow:Step 1955, mean_score: 19.765625\n",
      "INFO:tensorflow:Step 1960, mean_score: 19.781250\n",
      "INFO:tensorflow:Step 1965, mean_score: 19.781250\n",
      "INFO:tensorflow:Step 1970, mean_score: 19.781250\n",
      "INFO:tensorflow:Step 1975, mean_score: 19.781250\n",
      "INFO:tensorflow:Step 1980, mean_score: 19.781250\n",
      "INFO:tensorflow:Step 1985, mean_score: 19.781250\n",
      "INFO:tensorflow:Step 1990, mean_score: 19.781250\n",
      "INFO:tensorflow:Step 1995, mean_score: 19.781250\n",
      "INFO:tensorflow:Step 2000, mean_score: 19.781250\n",
      "INFO:tensorflow:Step 2005, mean_score: 19.781250\n",
      "INFO:tensorflow:Step 2010, mean_score: 19.781250\n",
      "INFO:tensorflow:Step 2015, mean_score: 19.781250\n",
      "INFO:tensorflow:Step 2020, mean_score: 19.781250\n",
      "INFO:tensorflow:Step 2025, mean_score: 19.781250\n",
      "INFO:tensorflow:Step 2030, mean_score: 19.781250\n",
      "INFO:tensorflow:Step 2035, mean_score: 19.781250\n",
      "INFO:tensorflow:Step 2040, mean_score: 19.781250\n",
      "INFO:tensorflow:Step 2045, mean_score: 19.781250\n",
      "INFO:tensorflow:Step 2050, mean_score: 19.796875\n",
      "INFO:tensorflow:Step 2055, mean_score: 19.796875\n",
      "INFO:tensorflow:Step 2060, mean_score: 19.812500\n",
      "INFO:tensorflow:Step 2065, mean_score: 19.812500\n",
      "INFO:tensorflow:Step 2070, mean_score: 19.812500\n",
      "INFO:tensorflow:Step 2075, mean_score: 19.812500\n",
      "INFO:tensorflow:Step 2080, mean_score: 19.812500\n",
      "INFO:tensorflow:Step 2085, mean_score: 19.812500\n",
      "INFO:tensorflow:Step 2090, mean_score: 19.812500\n",
      "INFO:tensorflow:Step 2095, mean_score: 19.812500\n",
      "INFO:tensorflow:Step 2100, mean_score: 19.812500\n",
      "INFO:tensorflow:Step 2105, mean_score: 19.812500\n",
      "INFO:tensorflow:Step 2110, mean_score: 19.812500\n",
      "INFO:tensorflow:Step 2115, mean_score: 19.812500\n",
      "INFO:tensorflow:Step 2120, mean_score: 19.812500\n",
      "INFO:tensorflow:Step 2125, mean_score: 19.812500\n",
      "INFO:tensorflow:Step 2130, mean_score: 19.812500\n",
      "INFO:tensorflow:Step 2135, mean_score: 19.812500\n",
      "INFO:tensorflow:Step 2140, mean_score: 19.828125\n",
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      "INFO:tensorflow:Step 2150, mean_score: 19.828125\n",
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      "INFO:tensorflow:Step 2160, mean_score: 19.828125\n",
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      "INFO:tensorflow:Step 2170, mean_score: 19.828125\n",
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      "INFO:tensorflow:Step 2200, mean_score: 19.828125\n",
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      "INFO:tensorflow:Step 2210, mean_score: 19.828125\n",
      "INFO:tensorflow:Step 2215, mean_score: 19.828125\n",
      "INFO:tensorflow:Step 2220, mean_score: 19.828125\n",
      "INFO:tensorflow:Step 2225, mean_score: 19.828125\n",
      "INFO:tensorflow:Step 2230, mean_score: 19.828125\n",
      "INFO:tensorflow:Step 2235, mean_score: 19.828125\n",
      "INFO:tensorflow:Step 2240, mean_score: 19.843750\n",
      "INFO:tensorflow:Step 2245, mean_score: 19.843750\n",
      "INFO:tensorflow:Step 2250, mean_score: 19.843750\n",
      "INFO:tensorflow:Step 2255, mean_score: 19.843750\n",
      "INFO:tensorflow:Step 2260, mean_score: 19.843750\n",
      "INFO:tensorflow:Step 2265, mean_score: 19.843750\n",
      "INFO:tensorflow:Step 2270, mean_score: 19.843750\n",
      "INFO:tensorflow:Step 2275, mean_score: 19.843750\n",
      "INFO:tensorflow:Step 2280, mean_score: 19.843750\n",
      "INFO:tensorflow:Step 2285, mean_score: 19.843750\n",
      "INFO:tensorflow:Step 2290, mean_score: 19.843750\n",
      "INFO:tensorflow:Step 2295, mean_score: 19.843750\n",
      "INFO:tensorflow:Step 2300, mean_score: 19.843750\n",
      "INFO:tensorflow:Step 2305, mean_score: 19.843750\n",
      "INFO:tensorflow:Step 2310, mean_score: 19.843750\n",
      "INFO:tensorflow:Step 2315, mean_score: 19.843750\n",
      "INFO:tensorflow:Evaluating metric mean_reward/eval/sampling_temp_0.5_max_noops_0_unclipped\n",
      "2019-03-22 16:12:57.935045: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0\n",
      "2019-03-22 16:12:57.935160: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
      "2019-03-22 16:12:57.935189: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0 \n",
      "2019-03-22 16:12:57.935209: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N \n",
      "2019-03-22 16:12:57.935553: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10754 MB memory) -> physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7)\n",
      "INFO:tensorflow:Using DummyPolicyProblem for the policy.\n",
      "INFO:tensorflow:Setting T2TModel mode to 'train'\n",
      "INFO:tensorflow:Using variable initializer: orthogonal\n",
      "INFO:tensorflow:Transforming feature 'input_action' with symbol_modality_6_64.bottom\n",
      "INFO:tensorflow:Transforming feature 'input_reward' with symbol_modality_3_64.bottom\n",
      "INFO:tensorflow:Transforming feature 'inputs' with video_modality.bottom\n",
      "INFO:tensorflow:Transforming feature 'target_action' with symbol_modality_6_64.targets_bottom\n",
      "INFO:tensorflow:Transforming feature 'target_policy' with identity_modality.targets_bottom\n",
      "INFO:tensorflow:Transforming feature 'target_reward' with symbol_modality_3_64.targets_bottom\n",
      "INFO:tensorflow:Transforming feature 'target_value' with identity_modality.targets_bottom\n",
      "INFO:tensorflow:Transforming feature 'targets' with video_modality.targets_bottom\n",
      "INFO:tensorflow:Building model body\n",
      "INFO:tensorflow:Transforming body output with identity_modality.top\n",
      "INFO:tensorflow:Transforming body output with identity_modality.top\n",
      "2019-03-22 16:13:12.260846: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0\n",
      "2019-03-22 16:13:12.260981: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
      "2019-03-22 16:13:12.261059: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0 \n",
      "2019-03-22 16:13:12.261099: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N \n",
      "2019-03-22 16:13:12.261613: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10754 MB memory) -> physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7)\n",
      "2019-03-22 16:13:12.493082: W tensorflow/core/platform/cloud/google_auth_provider.cc:178] All attempts to get a Google authentication bearer token failed, returning an empty token. Retrieving token from files failed with \"Not found: Could not locate the credentials file.\". Retrieving token from GCE failed with \"Cancelled: GCE check skipped due to presence of $NO_GCE_CHECK environment variable.\".\n",
      "INFO:tensorflow:Restoring checkpoint gs://tensor2tensor-checkpoints/modelrl_experiments/train_sd/142/policy/model.ckpt-171992\n",
      "2019-03-22 16:13:12.556955: W tensorflow/core/platform/cloud/google_auth_provider.cc:178] All attempts to get a Google authentication bearer token failed, returning an empty token. Retrieving token from files failed with \"Not found: Could not locate the credentials file.\". Retrieving token from GCE failed with \"Cancelled: GCE check skipped due to presence of $NO_GCE_CHECK environment variable.\".\n",
      "INFO:tensorflow:Restoring parameters from gs://tensor2tensor-checkpoints/modelrl_experiments/train_sd/142/policy/model.ckpt-171992\n",
      "2019-03-22 16:13:12.651009: W tensorflow/core/platform/cloud/google_auth_provider.cc:178] All attempts to get a Google authentication bearer token failed, returning an empty token. Retrieving token from files failed with \"Not found: Could not locate the credentials file.\". Retrieving token from GCE failed with \"Cancelled: GCE check skipped due to presence of $NO_GCE_CHECK environment variable.\".\n",
      "2019-03-22 16:13:12.715180: W tensorflow/core/platform/cloud/google_auth_provider.cc:178] All attempts to get a Google authentication bearer token failed, returning an empty token. Retrieving token from files failed with \"Not found: Could not locate the credentials file.\". Retrieving token from GCE failed with \"Cancelled: GCE check skipped due to presence of $NO_GCE_CHECK environment variable.\".\n",
      "2019-03-22 16:13:12.816774: W tensorflow/core/platform/cloud/google_auth_provider.cc:178] All attempts to get a Google authentication bearer token failed, returning an empty token. Retrieving token from files failed with \"Not found: Could not locate the credentials file.\". Retrieving token from GCE failed with \"Cancelled: GCE check skipped due to presence of $NO_GCE_CHECK environment variable.\".\n",
      "INFO:tensorflow:Step 5, mean_score: 0.000000\n",
      "INFO:tensorflow:Step 10, mean_score: 0.000000\n",
      "INFO:tensorflow:Step 15, mean_score: 0.000000\n",
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      "INFO:tensorflow:Step 55, mean_score: 0.000000\n",
      "INFO:tensorflow:Step 60, mean_score: 0.000000\n",
      "INFO:tensorflow:Step 65, mean_score: -0.031250\n",
      "INFO:tensorflow:Step 70, mean_score: -0.031250\n",
      "INFO:tensorflow:Step 75, mean_score: -0.031250\n",
      "INFO:tensorflow:Step 80, mean_score: -0.031250\n",
      "INFO:tensorflow:Step 85, mean_score: -0.031250\n",
      "INFO:tensorflow:Step 90, mean_score: -0.031250\n",
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      "INFO:tensorflow:Step 380, mean_score: 3.890625\n",
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      "INFO:tensorflow:Step 460, mean_score: 4.890625\n",
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      "INFO:tensorflow:Step 635, mean_score: 6.875000\n",
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     ]
    }
   ],
   "source": [
    "game = 'pong'\n",
    "run_dir = get_run_dir(game, 1)\n",
    "!python -m tensor2tensor.rl.evaluator \\\n",
    "  --loop_hparams_set=rlmb_long_stochastic_discrete \\\n",
    "  --loop_hparams=game=$game,eval_max_num_noops=8,eval_sampling_temps=[0.5] \\\n",
    "  --policy_dir=$run_dir/policy \\\n",
    "  --eval_metrics_dir=pong_pretrained \\\n",
    "  --debug_video_path=pong_pretrained \\\n",
    "  --num_debug_videos=4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WKWPdwP8BW_v",
    "colab_type": "text"
   },
   "source": [
    "The above command will run a single evaluation setting to get the results fast. We usually run a grid of different settings (sampling temperatures and whether to do initial no-ops). To do that, remove `eval_max_num_noops=8,eval_sampling_temps=[0.5]` from the command. You can override the evaluation settings:\n",
    "\n",
    "```\n",
    "  --loop_hparams=game=pong,eval_max_num_noops=0,eval_sampling_temps=[0.0]\n",
    " ```\n",
    " \n",
    " The evaluator generates videos from the environment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "At9LC5rxFyv2",
    "colab_type": "code",
    "outputId": "983b0e7a-2700-4e4a-d776-03c459669770",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1.553253830168E12,
     "user_tz": -60.0,
     "elapsed": 4036.0,
     "user": {
      "displayName": "Piotr Kozakowski",
      "photoUrl": "",
      "userId": "01014928596539690143"
     }
    },
    "colab": {
     "resources": {
      "http://localhost:8080/nbextensions/vid.mp4": {
       "data": 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       "ok": true,
       "headers": [
        [
         "content-type",
         "video/mp4"
        ]
       ],
       "status": 200.0,
       "status_text": ""
      }
     },
     "base_uri": "https://localhost:8080/",
     "height": 501.0
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <video width=\"640\" height=\"480\" controls>\n",
       "      <source src=\"/nbextensions/vid.mp4\" type=\"video/mp4\">\n",
       "    </video>\n",
       "  "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 24,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "play_video('pong_pretrained/0.avi')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "U-SyGcZBCmPn",
    "colab_type": "text"
   },
   "source": [
    "# Train your policy (model-free training)\n",
    "Training model-free on Pong (it takes a few hours):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "WIQazd5aCocc",
    "colab_type": "code",
    "outputId": "0a440c18-affc-4b2a-d6e1-c3cda84465bc",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1.553254256733E12,
     "user_tz": -60.0,
     "elapsed": 19957.0,
     "user": {
      "displayName": "Piotr Kozakowski",
      "photoUrl": "",
      "userId": "01014928596539690143"
     }
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1516.0
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
      "For more information, please see:\n",
      "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
      "  * https://github.com/tensorflow/addons\n",
      "If you depend on functionality not listed there, please file an issue.\n",
      "\n",
      "2019-03-22 11:30:42.987149: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2300000000 Hz\n",
      "2019-03-22 11:30:42.987392: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x30323c0 executing computations on platform Host. Devices:\n",
      "2019-03-22 11:30:42.987491: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): <undefined>, <undefined>\n",
      "2019-03-22 11:30:43.082876: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2019-03-22 11:30:43.083442: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x3032100 executing computations on platform CUDA. Devices:\n",
      "2019-03-22 11:30:43.083493: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): Tesla K80, Compute Capability 3.7\n",
      "2019-03-22 11:30:43.083843: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties: \n",
      "name: Tesla K80 major: 3 minor: 7 memoryClockRate(GHz): 0.8235\n",
      "pciBusID: 0000:00:04.0\n",
      "totalMemory: 11.17GiB freeMemory: 11.10GiB\n",
      "2019-03-22 11:30:43.083879: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0\n",
      "2019-03-22 11:30:43.475526: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
      "2019-03-22 11:30:43.475601: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0 \n",
      "2019-03-22 11:30:43.475629: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N \n",
      "2019-03-22 11:30:43.476026: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:42] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
      "2019-03-22 11:30:43.476131: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10754 MB memory) -> physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7)\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensor2tensor/rl/envs/py_func_batch_env.py:122: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "tf.py_func is deprecated in TF V2. Instead, use\n",
      "    tf.py_function, which takes a python function which manipulates tf eager\n",
      "    tensors instead of numpy arrays. It's easy to convert a tf eager tensor to\n",
      "    an ndarray (just call tensor.numpy()) but having access to eager tensors\n",
      "    means `tf.py_function`s can use accelerators such as GPUs as well as\n",
      "    being differentiable using a gradient tape.\n",
      "    \n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensor2tensor/utils/t2t_model.py:1358: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/function.py:1007: calling Graph.create_op (from tensorflow.python.framework.ops) with compute_shapes is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Shapes are always computed; don't use the compute_shapes as it has no effect.\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensor2tensor/layers/common_layers.py:277: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensor2tensor/models/research/rl.py:598: conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.conv2d instead.\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensor2tensor/models/research/rl.py:602: flatten (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.flatten instead.\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensor2tensor/models/research/rl.py:603: dropout (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.dropout instead.\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensor2tensor/models/research/rl.py:604: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.dense instead.\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensor2tensor/layers/common_layers.py:2887: multinomial (from tensorflow.python.ops.random_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.random.categorical instead.\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensor2tensor/rl/ppo_learner.py:479: Print (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2018-08-20.\n",
      "Instructions for updating:\n",
      "Use tf.print instead of tf.Print. Note that tf.print returns a no-output operator that directly prints the output. Outside of defuns or eager mode, this operator will not be executed unless it is directly specified in session.run or used as a control dependency for other operators. This is only a concern in graph mode. Below is an example of how to ensure tf.print executes in graph mode:\n",
      "```python\n",
      "    sess = tf.Session()\n",
      "    with sess.as_default():\n",
      "        tensor = tf.range(10)\n",
      "        print_op = tf.print(tensor)\n",
      "        with tf.control_dependencies([print_op]):\n",
      "          out = tf.add(tensor, tensor)\n",
      "        sess.run(out)\n",
      "    ```\n",
      "Additionally, to use tf.print in python 2.7, users must make sure to import\n",
      "the following:\n",
      "\n",
      "  `from __future__ import print_function`\n",
      "\n",
      "2019-03-22 11:30:49.903512: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0\n",
      "2019-03-22 11:30:49.903591: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
      "2019-03-22 11:30:49.903620: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0 \n",
      "2019-03-22 11:30:49.903639: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N \n",
      "2019-03-22 11:30:49.903898: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10754 MB memory) -> physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7)\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/saver.py:1266: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use standard file APIs to check for files with this prefix.\n",
      "2019-03-22 11:30:51.335217: I tensorflow/stream_executor/dso_loader.cc:152] successfully opened CUDA library libcublas.so.10.0 locally\n",
      "mean_score: [0][0][0]\n",
      "^C\n"
     ]
    }
   ],
   "source": [
    "!python -m tensor2tensor.rl.trainer_model_free \\\n",
    "  --hparams_set=rlmf_base \\\n",
    "  --hparams=game=pong \\\n",
    "  --output_dir=mf_pong"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FbSjwVAtCvLY",
    "colab_type": "text"
   },
   "source": [
    "Hyperparameter sets are defined in `tensor2tensor/models/research/rl.py`. You can override them using the hparams flag, e.g.\n",
    "\n",
    "```\n",
    "--hparams=game=kung_fu_master,frame_stack_size=5\n",
    "```\n",
    "\n",
    "As in model-based training, the periodic evaluation runs with timestep limit of 1000. To do full evaluation after training, run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "jppi4FE5C2nB",
    "colab_type": "code",
    "outputId": "a10afb7c-edd6-4a93-eee4-e3876977e825",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1.553254412202E12,
     "user_tz": -60.0,
     "elapsed": 15104.0,
     "user": {
      "displayName": "Piotr Kozakowski",
      "photoUrl": "",
      "userId": "01014928596539690143"
     }
    },
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 4083.0
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
      "For more information, please see:\n",
      "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
      "  * https://github.com/tensorflow/addons\n",
      "If you depend on functionality not listed there, please file an issue.\n",
      "\n",
      "INFO:tensorflow:Overriding hparams in rlmf_tiny with game=pong,eval_max_num_noops=0,eval_sampling_temps=[0.5]\n",
      "INFO:tensorflow:Evaluating metric mean_reward/eval/sampling_temp_0.5_max_noops_0_unclipped\n",
      "2019-03-22 11:33:23.214052: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2300000000 Hz\n",
      "2019-03-22 11:33:23.214294: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x2d07020 executing computations on platform Host. Devices:\n",
      "2019-03-22 11:33:23.214335: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): <undefined>, <undefined>\n",
      "2019-03-22 11:33:23.309948: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2019-03-22 11:33:23.310546: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x2d067e0 executing computations on platform CUDA. Devices:\n",
      "2019-03-22 11:33:23.310585: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): Tesla K80, Compute Capability 3.7\n",
      "2019-03-22 11:33:23.310991: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties: \n",
      "name: Tesla K80 major: 3 minor: 7 memoryClockRate(GHz): 0.8235\n",
      "pciBusID: 0000:00:04.0\n",
      "totalMemory: 11.17GiB freeMemory: 11.10GiB\n",
      "2019-03-22 11:33:23.311027: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0\n",
      "2019-03-22 11:33:23.707039: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
      "2019-03-22 11:33:23.707114: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0 \n",
      "2019-03-22 11:33:23.707139: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N \n",
      "2019-03-22 11:33:23.707459: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:42] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
      "2019-03-22 11:33:23.707523: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10754 MB memory) -> physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7)\n",
      "INFO:tensorflow:Using DummyPolicyProblem for the policy.\n",
      "INFO:tensorflow:Setting T2TModel mode to 'train'\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "INFO:tensorflow:Using variable initializer: orthogonal\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensor2tensor/utils/t2t_model.py:1358: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "INFO:tensorflow:Transforming feature 'input_action' with symbol_modality_6_64.bottom\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/function.py:1007: calling Graph.create_op (from tensorflow.python.framework.ops) with compute_shapes is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Shapes are always computed; don't use the compute_shapes as it has no effect.\n",
      "INFO:tensorflow:Transforming feature 'input_reward' with symbol_modality_3_64.bottom\n",
      "INFO:tensorflow:Transforming feature 'inputs' with video_modality.bottom\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensor2tensor/layers/common_video.py:495: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "tf.py_func is deprecated in TF V2. Instead, use\n",
      "    tf.py_function, which takes a python function which manipulates tf eager\n",
      "    tensors instead of numpy arrays. It's easy to convert a tf eager tensor to\n",
      "    an ndarray (just call tensor.numpy()) but having access to eager tensors\n",
      "    means `tf.py_function`s can use accelerators such as GPUs as well as\n",
      "    being differentiable using a gradient tape.\n",
      "    \n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensor2tensor/layers/common_layers.py:277: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "INFO:tensorflow:Transforming feature 'target_action' with symbol_modality_6_64.targets_bottom\n",
      "INFO:tensorflow:Transforming feature 'target_policy' with identity_modality.targets_bottom\n",
      "INFO:tensorflow:Transforming feature 'target_reward' with symbol_modality_3_64.targets_bottom\n",
      "INFO:tensorflow:Transforming feature 'target_value' with identity_modality.targets_bottom\n",
      "INFO:tensorflow:Transforming feature 'targets' with video_modality.targets_bottom\n",
      "INFO:tensorflow:Building model body\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensor2tensor/models/research/rl.py:598: conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.conv2d instead.\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensor2tensor/models/research/rl.py:602: flatten (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.flatten instead.\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensor2tensor/models/research/rl.py:603: dropout (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.dropout instead.\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensor2tensor/models/research/rl.py:604: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.dense instead.\n",
      "INFO:tensorflow:Transforming body output with identity_modality.top\n",
      "INFO:tensorflow:Transforming body output with identity_modality.top\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensor2tensor/layers/common_layers.py:2887: multinomial (from tensorflow.python.ops.random_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.random.categorical instead.\n",
      "2019-03-22 11:33:24.564271: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0\n",
      "2019-03-22 11:33:24.564350: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
      "2019-03-22 11:33:24.564376: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0 \n",
      "2019-03-22 11:33:24.564410: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N \n",
      "2019-03-22 11:33:24.564687: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10754 MB memory) -> physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7)\n",
      "INFO:tensorflow:Restoring checkpoint mf_pong/model.ckpt-9\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/saver.py:1266: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use standard file APIs to check for files with this prefix.\n",
      "INFO:tensorflow:Restoring parameters from mf_pong/model.ckpt-9\n",
      "2019-03-22 11:33:24.985295: I tensorflow/stream_executor/dso_loader.cc:152] successfully opened CUDA library libcublas.so.10.0 locally\n",
      "INFO:tensorflow:Step 5, mean_score: 0.000000\n",
      "INFO:tensorflow:Step 10, mean_score: 0.000000\n",
      "INFO:tensorflow:Step 15, mean_score: 0.000000\n",
      "INFO:tensorflow:Step 20, mean_score: 0.000000\n",
      "INFO:tensorflow:Step 25, mean_score: 0.000000\n",
      "INFO:tensorflow:Step 30, mean_score: 0.000000\n",
      "INFO:tensorflow:Step 35, mean_score: 0.000000\n",
      "INFO:tensorflow:Step 40, mean_score: 0.000000\n",
      "INFO:tensorflow:Step 45, mean_score: 0.000000\n",
      "INFO:tensorflow:Step 50, mean_score: 0.000000\n",
      "INFO:tensorflow:Step 55, mean_score: 0.000000\n",
      "INFO:tensorflow:Step 60, mean_score: 0.000000\n",
      "INFO:tensorflow:Step 65, mean_score: -1.000000\n",
      "INFO:tensorflow:Step 70, mean_score: -1.000000\n",
      "INFO:tensorflow:Step 75, mean_score: -1.000000\n",
      "INFO:tensorflow:Step 80, mean_score: -1.000000\n",
      "INFO:tensorflow:Step 85, mean_score: -1.000000\n",
      "INFO:tensorflow:Step 90, mean_score: -1.000000\n",
      "INFO:tensorflow:Step 95, mean_score: -1.000000\n",
      "INFO:tensorflow:Step 100, mean_score: -2.000000\n",
      "INFO:tensorflow:Step 105, mean_score: -2.000000\n",
      "INFO:tensorflow:Step 110, mean_score: -2.000000\n",
      "INFO:tensorflow:Step 115, mean_score: -2.000000\n",
      "INFO:tensorflow:Step 120, mean_score: -2.000000\n",
      "INFO:tensorflow:Step 125, mean_score: -2.000000\n",
      "INFO:tensorflow:Step 130, mean_score: -2.000000\n",
      "INFO:tensorflow:Step 135, mean_score: -3.000000\n",
      "INFO:tensorflow:Step 140, mean_score: -3.000000\n",
      "INFO:tensorflow:Step 145, mean_score: -3.000000\n",
      "INFO:tensorflow:Step 150, mean_score: -3.000000\n",
      "INFO:tensorflow:Step 155, mean_score: -3.000000\n",
      "INFO:tensorflow:Step 160, mean_score: -3.000000\n",
      "INFO:tensorflow:Step 165, mean_score: -3.000000\n",
      "INFO:tensorflow:Step 170, mean_score: -4.000000\n",
      "INFO:tensorflow:Step 175, mean_score: -4.000000\n",
      "INFO:tensorflow:Step 180, mean_score: -4.000000\n",
      "INFO:tensorflow:Step 185, mean_score: -4.000000\n",
      "INFO:tensorflow:Step 190, mean_score: -4.000000\n",
      "INFO:tensorflow:Step 195, mean_score: -4.000000\n",
      "INFO:tensorflow:Step 200, mean_score: -4.000000\n",
      "INFO:tensorflow:Step 205, mean_score: -5.000000\n",
      "INFO:tensorflow:Step 210, mean_score: -5.000000\n",
      "INFO:tensorflow:Step 215, mean_score: -5.000000\n",
      "INFO:tensorflow:Step 220, mean_score: -5.000000\n",
      "INFO:tensorflow:Step 225, mean_score: -5.000000\n",
      "INFO:tensorflow:Step 230, mean_score: -5.000000\n",
      "INFO:tensorflow:Step 235, mean_score: -5.000000\n",
      "INFO:tensorflow:Step 240, mean_score: -6.000000\n",
      "INFO:tensorflow:Step 245, mean_score: -6.000000\n",
      "INFO:tensorflow:Step 250, mean_score: -6.000000\n",
      "INFO:tensorflow:Step 255, mean_score: -6.000000\n",
      "INFO:tensorflow:Step 260, mean_score: -6.000000\n",
      "INFO:tensorflow:Step 265, mean_score: -6.000000\n",
      "INFO:tensorflow:Step 270, mean_score: -6.000000\n",
      "INFO:tensorflow:Step 275, mean_score: -7.000000\n",
      "INFO:tensorflow:Step 280, mean_score: -7.000000\n",
      "INFO:tensorflow:Step 285, mean_score: -7.000000\n",
      "INFO:tensorflow:Step 290, mean_score: -7.000000\n",
      "INFO:tensorflow:Step 295, mean_score: -7.000000\n",
      "INFO:tensorflow:Step 300, mean_score: -7.000000\n",
      "INFO:tensorflow:Step 305, mean_score: -7.000000\n",
      "INFO:tensorflow:Step 310, mean_score: -8.000000\n",
      "INFO:tensorflow:Step 315, mean_score: -8.000000\n",
      "INFO:tensorflow:Step 320, mean_score: -8.000000\n",
      "INFO:tensorflow:Step 325, mean_score: -8.000000\n",
      "INFO:tensorflow:Step 330, mean_score: -8.000000\n",
      "INFO:tensorflow:Step 335, mean_score: -8.000000\n",
      "INFO:tensorflow:Step 340, mean_score: -8.000000\n",
      "INFO:tensorflow:Step 345, mean_score: -9.000000\n",
      "INFO:tensorflow:Step 350, mean_score: -9.000000\n",
      "INFO:tensorflow:Step 355, mean_score: -9.000000\n",
      "INFO:tensorflow:Step 360, mean_score: -9.000000\n",
      "INFO:tensorflow:Step 365, mean_score: -9.000000\n",
      "INFO:tensorflow:Step 370, mean_score: -9.000000\n",
      "INFO:tensorflow:Step 375, mean_score: -9.000000\n",
      "INFO:tensorflow:Step 380, mean_score: -10.000000\n",
      "INFO:tensorflow:Step 385, mean_score: -10.000000\n",
      "INFO:tensorflow:Step 390, mean_score: -10.000000\n",
      "INFO:tensorflow:Step 395, mean_score: -10.000000\n",
      "INFO:tensorflow:Step 400, mean_score: -10.000000\n",
      "INFO:tensorflow:Step 405, mean_score: -10.000000\n",
      "INFO:tensorflow:Step 410, mean_score: -10.000000\n",
      "INFO:tensorflow:Step 415, mean_score: -11.000000\n",
      "INFO:tensorflow:Step 420, mean_score: -11.000000\n",
      "INFO:tensorflow:Step 425, mean_score: -11.000000\n",
      "INFO:tensorflow:Step 430, mean_score: -11.000000\n",
      "INFO:tensorflow:Step 435, mean_score: -11.000000\n",
      "INFO:tensorflow:Step 440, mean_score: -11.000000\n",
      "INFO:tensorflow:Step 445, mean_score: -11.000000\n",
      "INFO:tensorflow:Step 450, mean_score: -12.000000\n",
      "INFO:tensorflow:Step 455, mean_score: -12.000000\n",
      "INFO:tensorflow:Step 460, mean_score: -12.000000\n",
      "INFO:tensorflow:Step 465, mean_score: -12.000000\n",
      "INFO:tensorflow:Step 470, mean_score: -12.000000\n",
      "INFO:tensorflow:Step 475, mean_score: -12.000000\n",
      "INFO:tensorflow:Step 480, mean_score: -12.000000\n",
      "INFO:tensorflow:Step 485, mean_score: -13.000000\n",
      "INFO:tensorflow:Step 490, mean_score: -13.000000\n",
      "INFO:tensorflow:Step 495, mean_score: -13.000000\n",
      "INFO:tensorflow:Step 500, mean_score: -13.000000\n",
      "INFO:tensorflow:Step 505, mean_score: -13.000000\n",
      "INFO:tensorflow:Step 510, mean_score: -13.000000\n",
      "INFO:tensorflow:Step 515, mean_score: -13.000000\n",
      "INFO:tensorflow:Step 520, mean_score: -14.000000\n",
      "INFO:tensorflow:Step 525, mean_score: -14.000000\n",
      "INFO:tensorflow:Step 530, mean_score: -14.000000\n",
      "INFO:tensorflow:Step 535, mean_score: -14.000000\n",
      "INFO:tensorflow:Step 540, mean_score: -14.000000\n",
      "INFO:tensorflow:Step 545, mean_score: -14.000000\n",
      "INFO:tensorflow:Step 550, mean_score: -14.000000\n",
      "INFO:tensorflow:Step 555, mean_score: -15.000000\n",
      "INFO:tensorflow:Step 560, mean_score: -15.000000\n",
      "INFO:tensorflow:Step 565, mean_score: -15.000000\n",
      "INFO:tensorflow:Step 570, mean_score: -15.000000\n",
      "INFO:tensorflow:Step 575, mean_score: -15.000000\n",
      "INFO:tensorflow:Step 580, mean_score: -15.000000\n",
      "INFO:tensorflow:Step 585, mean_score: -15.000000\n",
      "INFO:tensorflow:Step 590, mean_score: -16.000000\n",
      "INFO:tensorflow:Step 595, mean_score: -16.000000\n",
      "INFO:tensorflow:Step 600, mean_score: -16.000000\n",
      "INFO:tensorflow:Step 605, mean_score: -16.000000\n",
      "INFO:tensorflow:Step 610, mean_score: -16.000000\n",
      "INFO:tensorflow:Step 615, mean_score: -16.000000\n",
      "INFO:tensorflow:Step 620, mean_score: -16.000000\n",
      "INFO:tensorflow:Step 625, mean_score: -17.000000\n",
      "INFO:tensorflow:Step 630, mean_score: -17.000000\n",
      "INFO:tensorflow:Step 635, mean_score: -17.000000\n",
      "INFO:tensorflow:Step 640, mean_score: -17.000000\n",
      "INFO:tensorflow:Step 645, mean_score: -17.000000\n",
      "INFO:tensorflow:Step 650, mean_score: -17.000000\n",
      "INFO:tensorflow:Step 655, mean_score: -17.000000\n",
      "INFO:tensorflow:Step 660, mean_score: -18.000000\n",
      "INFO:tensorflow:Step 665, mean_score: -18.000000\n",
      "INFO:tensorflow:Step 670, mean_score: -18.000000\n",
      "INFO:tensorflow:Step 675, mean_score: -18.000000\n",
      "INFO:tensorflow:Step 680, mean_score: -18.000000\n",
      "INFO:tensorflow:Step 685, mean_score: -18.000000\n",
      "INFO:tensorflow:Step 690, mean_score: -18.000000\n",
      "INFO:tensorflow:Step 695, mean_score: -19.000000\n",
      "INFO:tensorflow:Step 700, mean_score: -19.000000\n",
      "INFO:tensorflow:Step 705, mean_score: -19.000000\n",
      "INFO:tensorflow:Step 710, mean_score: -19.000000\n",
      "INFO:tensorflow:Step 715, mean_score: -19.000000\n",
      "INFO:tensorflow:Step 720, mean_score: -19.000000\n",
      "INFO:tensorflow:Step 725, mean_score: -19.000000\n",
      "INFO:tensorflow:Step 730, mean_score: -20.000000\n",
      "INFO:tensorflow:Step 735, mean_score: -20.000000\n",
      "INFO:tensorflow:Step 740, mean_score: -20.000000\n",
      "INFO:tensorflow:Step 745, mean_score: -20.000000\n",
      "INFO:tensorflow:Step 750, mean_score: -20.000000\n",
      "INFO:tensorflow:Step 755, mean_score: -20.000000\n",
      "INFO:tensorflow:Step 760, mean_score: -20.000000\n"
     ]
    }
   ],
   "source": [
    "!python -m tensor2tensor.rl.evaluator \\\n",
    "  --loop_hparams_set=rlmf_tiny \\\n",
    "  --hparams=game=pong \\\n",
    "  --policy_dir=mf_pong \\\n",
    "  --debug_video_path=mf_pong \\\n",
    "  --num_debug_videos=4 \\\n",
    "  --eval_metrics_dir=mf_pong/full_eval_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "mDoR0C0ZKCOn",
    "colab_type": "code",
    "outputId": "aba41a4d-2957-4ea0-d511-eae7ea4e238e",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1.553254513355E12,
     "user_tz": -60.0,
     "elapsed": 3908.0,
     "user": {
      "displayName": "Piotr Kozakowski",
      "photoUrl": "",
      "userId": "01014928596539690143"
     }
    },
    "colab": {
     "resources": {
      "http://localhost:8080/nbextensions/vid.mp4": {
       "data": 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U2aIRq+bew64gtHymArgzox7IataLMEs58QAAACsBnntqRH+HsR7W7gx/uLfAQSDqjarp3zTZOCfEh+6Eb54T4PDy1sS+MHUHAAAAREGaf0nhDyZTAjf/++s+OA7zprt9FalqM30xdIjlcH2DZkkFtbEKP2KnwPRDHCudTxGL9Qj/prFtXdQZd/8kyvQgWVbBAAAAGkGenUURPN99TaObeIE6LTTxi495G3R7GPmQAAAAMgGevmpEf4k5No92pOx4d3tVe/jt/92QHqWDJ1gLa+IuwyWHPUwqtoP8fV9Ym9imBVegAAAAVEGaoUmoQWiZTBTxv/vhUdghqkJVKiFoDxnkwJdI5EfJ1rXs+3IWgFyiLClNqrjfdF5LRESoN+/5vYqeA8+ZDoM0LHM+p62tsd2sj1HySk6QgTd2RQAAAB4BnsBqRH+JNh+VxVwUJ+Y3lV+ryJZRr2sjHdcr6hMAAABLQZrDSeEKUmUwUsb/++A44DvMfbctqjDdsKqNf0xOA39UYyLAzYIZyyzAIzc51s3jIS5ItuDvEtTcwkuqq/36WLfiOMPxF1mVVotBAAAANwGe4mpEf4EPa4byEQdMkh18OfNdNbI0980SSbNKs1ZKN+RKPSfGS6ySQe8jTiCjRi2a1L2bI0AAAABTQZrkSeEOiZTAjf/74VHYIlYiKMdyQZmvfSW5HcZkgM1RFiY3gEH71z/3fVBzC8liqsmHHacePA4zlFgRGs62G32VHnZQ/DXMUNXZSe6AM+37MoEAAABCQZsFSeEPJlMCN//7w+5d2niwPbxnXcPGjmeLv2gdbVqjCSUoEJNhCpLEDmpftWkeZV+eoSYNoxZWdF8ucDxxQt1tAAAAUUGbJknhDyZTAjf/+8OhtVOQEg0JinGp/3aOKBnRKunbXz9YVqPZNGUQc6qywYwzQClzX3GGGmt+n+j6fKijedTiog/HS3groeaf1ZLhN7MSZwAAAEFBm0dJ4Q8mUwI3//vEgdmkZAQXhaU5Ka/mwO9DUeQ8nbhvyYsA1HsjVV69Yl5uJ7Z+5cENl5oKK+xgTk4zv6uteQAAAD1Bm2lJ4Q8mUwURPG/74DjgQlevXVZC8R8NNBU5lXz/5evdLx/xJ2R3nKEWhnQTJ2MhCGZTRB2wGJ2TPpnAAAAAMQGfiGpEf4exElW+Y0oRl/FZgSZ/zHaDUN2en4eI/45oExvCn0eIC8T7xwpFCCD22pAAAAA0QZuKSeEPJlMCN//74DjgJBslHpy/fK99xLLgEoSJoPeLiwa05OsKQgsTdDRtpBJScyBfZQAAAFtBm6tJ4Q8mUwI3//vBcoj4CbBCQT2RExz0aNVXfJZgEMRjG13pvbjo3lZ6TLZP87/4osMgfYBX228XW1pfmU8k2l4pXWkF2ONrCPtLhUtLA/Hya2TerrONeACzAAAAUEGbzEnhDyZTAjf/++A9RB901G+fHcU/WnLCfcsk74GCTFIVwJ+BLzEBzmOXsONP0vNgTAP2KwhTXJSx3MerL/qd/oYBMUklPzb3D+iSqTSoAAAAQEGb7UnhDyZTAjf/++A9RCB74KMJGvAkjJTldXY5HMHa5jfjN8NHs9hBd2fEIGPYdqirJKwimKLpiuNnGPY71cEAAABDQZoOSeEPJlMCN//74DjgQ+tokgudjQu5NRNWobxjhH6Kh3ypTnRXjwJga7RHFZxm4LSd8nQrtx1GfqIwsNleIlTJwQAAAF1Bmi9J4Q8mUwI3//vBnCJ2LA71GF25ddxrRcaDbZ6JrFKdXAbBcwenVA97XR386r26bMwGidvp2PmbPeLJeVTrLs40YO/0dkENxamY3tNtYwvFfXlRKOo9yyo8A4EAAABRQZpQSeEPJlMCN//7w3v8sjICMILXR5ECQLilrI3AMHhNoAl8fjML6XnsQAd3GcNYf/0Vg/WkUZMoFlfh0iQSiC4/lahyE8PO4Mqsxu0If+cpAAAASkGac0nhDyZTAjf//APDMcB3mPxGGZH0fTP3ycONiP81zu+xkY8BF29QMHZMJZp5xyqLnicMHS675xoi5qplhfLQsVW1n9dVmaldAAAAHkGekUURPN99TaObeIE82Q23ingUfeUAohKj+s7K1QAAADgBnrJqRH+HsRJVgDz377tnoNimjhTZvPY9yHf2fmv0wsOMF+wp88Zp9A47UCY9LcsoRhIHvx+VqgAAAD9BmrRJqEFomUwI3/vgHICtV4/N+3TLBHjt+Fcn7DWjz8soUTNhnfkqkcMHOJu59OaFxhuVf+ti1/1ybHdmF1AAAABfQZrVSeEKUmUwI3/7wf8MHhAR1o8LZVW6pfXYDg2dpGT5Rvg+Cl2lQj+IQxqhhQEfFhAQwlKUGoZJ0yY1+WX5L6BuA3zmdozX8D1TAHaS+xXRLIKO1jwkjAd/JNceRMEAAABNQZr2SeEOiZTAjf/8ESF/UQffCua7E+r+NL8HxgRR3d+rdy5oOmnMWV4zw6CD9Z7mbT2yUeu59zFDbP46SRq9klvWZ1ZbOSW+2vZf9fQAAABHQZsXSeEPJlMCN//74EfgEPLRNeKMrWaAkB9b5moj+s74rojfFold/VwZwZnjRlAT49c0fDe4ZV3UBIGBK9CK3ytLN0Y42oEAAABDQZs4SeEPJlMCN//74DjgOFPapEipwNQ5JLf/iFkT7vTC27zO0O1Hhr7Be9x5uGF5a9SD1TJTW/IfLAT7Nh7DJMuMeQAAAGpBm1lJ4Q8mUwI3//vBm7tPFge4qVrTYzB1Rf4j8SwaiUd3Vn91V8IbddfFnSdsq/LMm7+bcOxpHHp/+sF3L+oZtRUH+68r3BQroy9k+62TGFClrSWs36FP/9yav//StMk6n2lq+8O7DNsoAAAATEGbeknhDyZTAjf/+8Ohm1TlEJHbNU/mWRKFgYzLN2yQ8jXAB9wCzOsfbVP2lU1EY8QOgEzKqXo0R7iSjvfThP3Ejb6DhTio3axVsWsAAABXQZudSeEPJlMCN//77lcOA7zH4jDF8og00F+0xFfHR4VDZOJrgOjozJdR/bpg8saNaDwaxnfv4X/eRhAQ/CW8F20/hfAqOk7ganOxPkKbRibKABEgisd4AAAAIkGfu0URPN99VLWJAgSGMMD0PyKXcxUT3cFaf6NQH7WvfiEAAABEAZ/cakR/h7ESVb5jSubSAV8FnD8x4gGanIIpXwVIvISOGwpzao9rHxOAHmz8XBzh60OGZXgi//qWtWwRLulsxilCnuEAAABsQZvfSahBaJlMFPG//Blf8ICQaEccs93WPEeRPsjPuDf4gDC1lzaA9vG8/MDesnCCrO+qwrWg0Z041Lq6MnjtnVfjvIt0qF1hF5tlhQF/0d8e9aqpY2UcQgjUy9Hd3+CTWS0I7CWSzCsCaZnAAAAAFwGf/mpEf4jgOwqq40N+HXrqPTGH7PlEAAAAU0Gb4knhClJlMCN/++A9RBAtNsNSCFkg711//JWWvGFsLrGyK1H7zXz+lTaC4eBFVxUDg/Op1j5vlFjWLyVGxDTLWQWt7v06Slq9bNE1cmz/5OS1AAAAMEGeAEU0TN99Tam3C0rJYqxDWOyBOLJHIckz5MqYD+DjRZpqPdIIw0nQ9LZT+KG3YAAAACABniFqRH+BB5WG0Yk+JGFIqTc/MKybQhljSSoiM7L+dQAAARtBmiZJqEFomUwI39P1UQmO/CHKa5fG9B98DyBd2q//xdlw3GXkmq/tlCxKXGJhBrqImPX+Tw62HdRUoCKQH1/ixC6DIupugrK3/kza+ntSNKpOnTIb2YWwrbEfjsAUf/8Csikkf00Yy4JNpq0NT3r3G73dUSTcvN2hSQC1jpvA4PpqiSqoyt1rg4K5nEb2X3Ta2J5m01MrHsJWKap7ArXaL/fag2Yxz1DnrBGNF0N64688FzTsfy3aMFyBNTwHxDcOM5VxEYpgPbQx0UoRaoEjPSOfPfZWW0CeOiVTmPWdE40N3Fq2/t5cedvB6NP7Q1mjnmICrga1HKXTUnIgZVEERRT/lQNzJZZSENnwR7l7OYZdbPaS+0iJ0PvAAAAANkGeREURLJ/NXH5jrirbiTEhY9Y/tlMwwq1venEm0Vym9wIwyi7kBVs/cNPG6n7cE56ATPcwWQAAAHcBnmN0RH/N1YmW42fk1rr5dWrvg0hpI+7/lxaqBQ9u9qPMWMe//elYZNo+NLjg8cYBUA/d4tfmi8Qz2UXcEo8Lvkc59K0YQAe1JOntXnqOjM4McR1Gx6UrMCz14vMSat1IO5CZ6UXFyvmTyeshwVAiRewmoKI5gQAAACABnmVqRH+HrQBVU2y7OiaX26aU/4ah2bLxWm+Y90YzwQAAADxBmmdJqEFsmUwI3/wDwzHA7nIUz9EUj2ygWhqxgWMh9LPrZxgjmvIJeUBCqf7Hl5oCspr9BQrYw3VwQWEAAACvQZqJSeEKUmUwUVLG/8dHUh70tVYbmJAP5Yav75x8KgRRrVZqGBAEqyIrK6LRnrpZlSZkXNqRr0UFWwbqNCFOpYqLeS1RQ2PyEkC+dBVplJUfZiuhOQAvvreTkE7X7a6IRT6oN2yIUD3gEVfN9FOVcWA6Z8kXNa18/OFVTCJp5CwGEmxWr1WMNJva0wrVNHOS92/kbWx21BB21E9sgNNFnB189Z9RulpZSpgE8SAYOAAAACABnqhqRH/N9J+2J1XcPpDCijwctlpIFM7gAfQRhV49uAAAAHJBmq1J4Q6JlMCN//kgYHc058NIdrA4tZt28lo3D3meW1zCAqwidHRRteHMGFHB/D9M0MCX00dwKKmnXGyIjSiIo/6dF3Oz1KEMYwbbPPtUPovm6LbF9t2EyWlXZ2AhyytqaBHYUktsnfMGjBar9/t8GUEAAAA3QZ7LRRU8n36t1fJ1HlQISEIEfpaQSyOL40QRE9Vs9zvdbKrtZIXDJfPzuaeN4LIza42Lez2eIAAAACcBnup0RH+HsPlbmN8Ea52A1bNIOYqtlLjflwD1gV/uybFUQRKpUOAAAAAdAZ7sakR/hNQrQyxiUawcG/LrF0fwKrQNveY8OGEAAABTQZrvSahBaJlMFPG//APDOoh1u+/wA6/FZcjU/jvz0FUQ/BieNWgawp32ZtUYldJMEVjFO412vb48n9u19LQOw5J+xi/1yuC/hu68Dsm1661iVvEAAAApAZ8OakR/h7DkMUQbpFoFWKV9q8E5k5LkELQbh4pZCjhTQyjdPLum4gMAAABAQZsRSeEKUmUwUsb/++A44EJXr11WJCw7ECvX60Zrh07UIdiv7aDfa0prRgD6jv/0Wc4+K9FDJsKP/KqGWZJW8AAAADABnzBqRH+JfH18ZqS57WtbAe/IO8nxI2HXbfpO2Nkfx7T6UrglP5FtwO/Jq+dpJWAAAABuQZszSeEOiZTBRMb/+8dnvoPCAeCb2/rWCrRRMAZhYjQsyCGOaWbOiXjQ45Hd4KBD69SXBo/G1jQPyXicID3GsLjYV+7vn2Od6v4G+WQqMHMoRfr8vCzfPrEsm0u23BOlwjpYpIOmf//S7gManfcAAAAYAZ9SakR/iOA7CrDrXGjeuosMdCPQVofsAAAAY0GbVUnhDyZTBTxv++A44DvT323Jx0SqVNJ82IBpyyo7DXCG+RxgT1NMsMLKQax8K4yQWP/71kziHHgGscwzo5L2jLizRX09ig4TiJCfNOrU3b1tM7YaGwugP+oXNvCezwnqQAAAAC0Bn3RqRH+IvbTI3y+IN9Ohxp8NHgN3aoRoF0CyJ7Kizh4lPfrWVzPrEIihMfEAAAA1QZt2SeEPJlMCN//74DjgQ+tokgude4jclMN4O+jlwgqt2WyQlDcvOp1uGtyVo8FNYf8DxygAAABYQZuXSeEPJlMCN//7w+5d2niwPcY4fRW/HK6IFjrt0h2pzlDEeWsYZI51tgKX93CPYsiPqPq3X5B1aWcUk55oKJGxvjXmYgG2872vqJP978jdfl4d7IIZYQAAAGpBm7pJ4Q8mUwI3//vD7kjX2UR2WhZ7zwuAd6E2YZpVsaXqf/9ms8pdq2AcVeqHU+2duGYw6XN6gJJPmwjaRjOqn/963zMAo7BbLbQ0xWrehzgNm86QkFh5F4CJPtfJrXi2qfvexEjl9SQZAAAAPEGf2EURPN99VLXrSyKjUixfkxdL90ti/7QhhH5aJuIgfKR9AwA9eKagET/7LDiGl5JEDu2HE71HainiPAAAACQBn/lqRH+HrQBVU2zahqo4JDrA0Wsth2bQ6i1pu5g6vv10b/kAAABIQZv7SahBaJlMCN/7/bCFg4EB8mDknVh3Li4nF+3GJT89JkDhouv2updeuOWXeNe5p0zRxNBaBS36FPmUKGpjSDMCDJPbtDwsAAAAREGaHUnhClJlMFESxv/74VHYRK6xf9nuJ/mW8vBkEYktQx5ng60k0zpkEVATXt/+Il4+t1Y/gWE4ff2KkBugUa5RlCmVAAAAGQGePGpEf4k2H5WycQ87dp8VYGfF0/IU9Z0AAABiQZohSeEOiZTAjf/7/gOFgQm+EEcUp0ZlHiPiNyhbnVoqZ+8senN2fT/GHn0i0DJAHVHDeAAPPdb6+08ywf9ggCftEN0KTRcMP6zvyMME1GrdoLnzWjL9J+Li8Z7zUjdev4AAAABAQZ5fRRU8n3sgptQ/K33IpJWP93HYo6zTTQPcVWgvT/tt2dtXG8r9XMctnwTJYMXBZrnQX8wcobNb8TVLLKjwQAAAACsBnn50RH+HsPlaZzJzUbRAKINcL+9T91jJ1Gd5JZXKKMYQPHi5kXraXljhAAAAIgGeYGpEf4TUK0IOqy+T8lgawG4WgIsZVeplQdYOkTmpMeAAAABJQZpjSahBaJlMFPG//AgTwyAUQ/9Od+649emXNJgblrSImgV1dWIgYyUeX0WkGz1oPOdc6UKgLvfxF2udL9r+3vyOJ3zOsvfmOQAAADQBnoJqRH+HsOQxRCCMCTKsMv80pUcMEWMaBnG+bZLo/o7gAggPnWMrK71nR0MaLVpDsOS6AAAAMUGahUnhClJlMFLG//vgHIBhjoEviMguc3ZwqTYQjvB+dEBD+tTd/LD/U79/aezoU08AAAAyAZ6kakR/h7ESVYA0yC7NG/OtKYdKqWLeGmJMPcZhbIRuFc+rxG1R7C8fxWQE6y89g50AAAE9QZqoSeEOiZTAjf/V0GkzH5jjHogSzwV8Ur8f+Ec2ZqF42vRmT3+0XBKgmcsowSJtIfdoeCmqACWCkOKlAnt3SySxtVHTz5bNRvdUm7O7h2Utc/NeW1ORiPi5ahyiTp6pzvTB7gpoYxSr8OJvwgt07bwjElPFl2/DXqzJ7mlN8Ko4FuS3th/HSK3m8xjdy9Pd2HG/G/ifzmmcbp4pvEyDSaJK1W2uFie5GND/WdfCyg3jjBDikafVsZZsB9db1ELXgOz6/mtW1PO65LJeMpnL03zTJn4LERpRc9KcXLD8eihY+pbPtstJ8ymI7vFDm0fdHk9VglqCfCuOm5LOR1Xte0qY4v0ZF497FEg6J2gXBmk0KeY7+LdmgHYEcz0qRZkyA93gRlWQCcg2bdHG3R1RsZFfPnVqyGpeaMJQsD8AAABgQZ7GRRU839CfjYe8gADwWSBEZ0dUg8ycG32E01qLxeAqrHnZPUJ5Lyx+8BUnDWj0XZtSKfpUSpAhT/szG+CsJq3bkVF7YO0bZ1YvXCQjDz1cgeQteorKqspdc1Fi4XCBAAAAGQGe52pEf6jUd6g4XFW5qqvsTJAPqVNfayAAAACQQZrsSahBaJlMCN/UtXwfA78SpsbvqlpyWOlTh6QrFvdRJjjmVnCQz861RBv+whwL1CGfpCv6GfEMxGCpC9HYQQUE0dQXgCNgcSS6mD0fn/+UCPvjhBVhxi7NZ4U2QtlGo9RKKkNi+HxgY6TR164zEIP510Utju+3WmWp8LCMIzVG7g95lqRKQwTZXOyoblZEAAAAPUGfCkURLJ+megsfuYeXW7Xti51RtkOkbffzL/ASgnb6g1Hr5xJiwX7/7XlPkdirFvrO694xVrxkHWvRxaEAAAAdAZ8pdER/gGwq2QxVj1MPQwZguW2S81h08qjXqxAAAAArAZ8rakR/3Cspb5/QadRcLmNZSh6g5dIyMVdtWcxCmcKN8VSOxyyA+VX1BgAAAD1BmzBJqEFsmUwI3/1ikeaEjHOd45AIQeOuawfhOqyYDx3N0zAkiYWZShFSr60ONAwyK1zoY65cTb4j02w5AAAALkGfTkUVLJ99jGQnHgDM8ln+aBB+E2IcxAM5SUB34rzqVNrLOXd+6bM2xuY4uoEAAAAbAZ9tdER/gw4tjgY4RdE2+ViqVUsAWJkRCr/RAAAAFgGfb2pEf4EHlYPO0DBg6RcG870j464AAABUQZtxSahBbJlMCN/OlXvvG5yLTHBTaPrAMVQaCfuKj0j5S7bXX6yX7qQf5RBo8No2FXkM/qWMiQOljutkadwskYcyP7bS28Hd6oB0nybXNXLow16IAAAAR0GblUnhClJlMCN//R5j7oKUrIUIBCbU00bLa+xJg3Zc+8xHG2utLSrY8Lzn10Z4sXQk4HLrp+BfVVniEmXvgjPxoP1/+SNTAAAAM0Gfs0U0TJ9+tCxJ1Gn0jDb8mTrU/wrX4mC1oGx1U6eFX27MccNul90QPfzPIXxTBWHVoAAAACUBn9J0RH+Jee66QMi55jOocyx//JCSfKN8+wOm/wGvvJAu+yXAAAAAGwGf1GpEf4TUKzbRXPU6nraxj9iMWSV9IgbTbQAAAFBBm9ZJqEFomUwI3/wICGIsD2OziqyuzNNpqzvABec/jYnGKj/psjvJAnKx6B88nuodchjeQKU8/Sf4fxPeI3Bur+63wb/oqD2JcdFb2ce6gAAAAFFBm/hJ4QpSZTBREsb/+8PuSK8cBB148viFWw/NHqyGs+rm2ceC8lLcGprCfFWoP4uRxFnVQBO6X5Km47W1B7f7HR4MpVRMW/C/8Bu03QteuBkAAAAjAZ4XakR/h60AVVNs3VhXxKqVaJQ3kpIsIMaGLSdIpeJrRq8AAABIQZoZSeEOiZTAjf/7w6KlpQgJLKb7CVQpIKCX2GtUUOMn6NT46JbGsSJDqhqGAUlsdEP5mjnTYaodhrLt7P8/yMykEazbfVizAAAAM0GaOknhDyZTAjf/++AcgTWnNhxEl9OeuJnrXVsKR/mhF9lVb2NCffZ/IdUboGwovrVQewAAAFlBmltJ4Q8mUwI3//vhUdhAycJLcpdZgvrBHbuS0ELA2qR1GWV/sKmX9lqbykcrOwXH6Qp5yOSFSCovTOHfR4H66+O3WFhrSEGUCxI2T37abEC4N7SLdnmMQAAAAEpBmn1J4Q8mUwURPG/74Djgdzkgaa9RAdjKXG4f20PXgc0gPoX4Vj0Ucek2XSjMdrwZw3mpyUgcp/uoOq3e0VHPfh7Zubi3pdTi5QAAACoBnpxqRH+HsRNxvnokvr1IBRBrhf3qfussoHJ2FXwia8L0og/7L34YHvkAAAAxQZqeSeEPJlMCN//74ESoSq306qyBH0bjlOnxLGbrkGJ2Ws39CdVP/9jJZ6U/XdORQAAAAElBmqBJ4Q8mUwURPG/8A6IrIENlUdGDC2rGxYKvs0Zv/PoytQYJJmfkiM01RixdLevQCF9AnGptUZtShDxvMVuLKVNUIVwk3Z75AAAALQGe32pEf4egL3/1jAGor8/AQZ6N6eggOUr7LqhLAXV7FfOYIjSYAD8KRrCokQAAAD9BmsNJ4Q8mUwI3//wC0LvCDOPxGGZH0fTTrqCs2EFRlT6/L9YpDrTovL58/OT2vGOyWsqXu1aV/a7fySUgV4AAAAAeQZ7hRRE8331No5t4g23Ywzxi4929Dj8IQsijl2gJAAAALAGfAmpEf4k5No92peKr/htFkwdNuhonMp0Ro1YlnQcnCewvp2SzmqBS8yiAAAAAXUGbBUmoQWiZTBTxv/vhUdhCtFQj3CF8I0Z8KP7PO2SHsB6lgIAgPVTE9oxFKS1kdE31Ian66qP+yqf9hh734mZwin70dY7QE5chfscqW3JQE3iewy17sVw7qZM5wQAAAB0BnyRqRH+JNh+VxVvSpS/NWVqjqTRtbJ2NrINTKwAAAQVBmylJ4QpSZTAjf8EwsBpzW3+EphZT+sMjYK72pqFmMagiJ/6RxdCOrdIwWTYN0R3uDTSb/XRdzEteeehW0X8cK0bfRzlGLg54Bih5AVBjp8JIdS+frbm7i9b7XCr5CBhzbRO0uWlX13WMTPZSuvGoTUMrRVgG628K7rQSE3qBkBMgw+CcCNefjGn6Zkx+q5pfh8Mt0QqMzqj7mnSuGudADa/xLYXydiZCYQ7/ewnsEpGkOK9yVq6rK50LcAUK+I3NgBMWmbuoV9kh2ewa2IxwBTrP8/IrrKTnIq0axfEx25XaS1gBYRb6kDVOAPsog/1jZSwYZ4GatQjuLuVTjthxX68wVF8AAABLQZ9HRTRMn9O9y352oflZsxy6BAxflZu0pxOibLVcNnE+wS7cYiWlcJ/txGIxRrd+RdfvjrOjPfDoy3gMEaOA1ktKWZzpAcG2KsiDAAAAKQGfZnREf4ew+VpnMnMtuJjFWqP/yRU8Vfsm/SA+y58AHgHPfjY6Ic5gAAAAKQGfaGpEf9fFlYagcVz5uBzD/QIPWYbzU9vkHWdHRQQyeCK9nXQ4I2EsAAAAikGba0moQWiZTBTxv8gMsVZymC78zcwH990XdZZmZzJ4wtrIvyzMXIOULxsiqy3MuQ/I48r4n72WrGhLg75YH6WEsBqONLX6BjtJ8TjNYAtjYKLrQKQl6vGS9ZfD13YsDE3Jvu673GbAbsZboZ/13vUZzF8zSZOV9IwR2roprdQI0feIeu7cnUYSMQAAACgBn4pqRH+o0ZioFg2l6PKcHO/zygwvcEiwx436ZjD+vu4Dcnlr22pAAAAAQUGbjUnhClJlMFLG//vgOOCFHL75XjgbP/nLa3NdHqfl9wTp94oAO8T7lQOqg3O0Mx03n3NRR7pYlze6hVEf4hs4AAAAMgGfrGpEf4ERwY+Gt77r+yEJ4SvPAYi+RMSplok3KwSDGQpSuB95O2ZN6aV0x9y74RwxAAAAZEGbr0nhDomUwUTG//vHT9FLCBA2nxris8BwI6TDKNzQGtw992rw5prohMGR5qiMwrOY4KxeyFVh4Z5nsHZDDLBJCMqHd6qBQyfBqvHpiFjePAt/3JK/Ps50Y7xjr5QVjDmLLnEAAAAWAZ/OakR/hNQrQmu509DykZGMqHC4wQAAAJZBm9JJ4Q8mUwI3/8evureZ7lc3KXdmxnQXgMijUWF3ZgneFpdm9nM3WOKWFmTpBzrecoyXK1AK3/TzKgoznmiqaxTbi+HkZzv39k56Xd8Rwloi2NmfZ/xAvKYkrgc7Yj9489acf/vZrf+rHiwEDuf4P34wfjU87tMB0gm0+ejzliNtxzSXYbeAmmP64RveFZ816fJhWEQAAAAuQZ/wRRE839BbCo5vTqlWMLNw7JP9qz7O6uD4nu1gs/O9P9c5x1S1ytTVzTI2+AAAAB0BnhFqRH+BB5XFIVzyB9wJwDYiu0A8ZNtKI6DGeQAAAEJBmhZJqEFomUwI3/18y04SEQRzJO8XoEYWKQTqvMsTVgq6JgxNfx+qCvDs/Eb18miZtc9keAkyj4LDbnj+9YUtWYAAAAAzQZ40RREsn32aKglvf2C9/3iUkf/5fFdsKyz6B6M9ZyCIDP+BGGOtWUef8hos++ANyp2AAAAAKwGeU3REf4exAhsvjHgSnTXrK0yG7hQMOWbFoITHQQMMz45J+HIhTgvG3EEAAAAiAZ5VakR/hnW4Mf925RATlfTwJxSqw6b0//nC+VfvnPeXgAAAACpBmldJqEFsmUwI3/vuVw4CBhG+4lg3Es59Okupr9RDias/wbkJjrkoqxEAAAAuQZp4SeEKUmUwI3/74DjgJAb7CXLbzf5nq0/dretTIIHlFI1cAGN8WOxHbKwvXQAAAH5BmplJ4Q6JlMCN//vBGT5YQEgiNI4YfqwxY7pw7Y2LpcvMCmuuwZiEyCprEorTJzgFnI1fTLxSf+rSP+efSERrkuP/Gkp1S5WCPd1cY+R8nU2/Ecise1u7zn7FazbzJRLufU5Eeprb8Vk57B7YxKp6pppaYCg0Zui1dDn3N58AAABvQZq7SeEPJlMFETxv++A44DvMfbdU1hKfbdntKLt5PPLOQy9ifPEv2J3HrARgxvABi0D1MxB+8N2XXdutIkzk+EXbiNPhxpbHQRvoMb11pMEpF0dzIxjytdVY2pHHvt4vqc8m5kctbxoCGPrGzq7BAAAAOgGe2mpEf4l71gLl090r4xvvAf/rji6zKN85FiDpcbWZht1n1na/FcE7BBvTXNdoIy6yLTUk5BdVxIAAAABWQZrcSeEPJlMCN//74DjgQ+tokgubHJTmcS2bnfPkzdAbeefDjnKfV7MFzXTULGE7gZCZQWfJ6qOVkkF8AtxRAEmR69//H0c3PlHntplsbW9FUKAYTRcAAABbQZr9SeEPJlMCN//7w+5d2niwPcKLWmwiVkBeI/d3EBe4uOe41g3mCl0o4kirBlvctec2F+QjLJhaKWtN6qGqc6uLOLJ+QX+s7b6lWU0b6H0eQNGAmGh+ktnyUwAAAGJBmwBJ4Q8mUwI3//vDoZtTkBBrgFeRatJUfKwOw/xfRodt9aPbXcnC8nF6plsAvzoY3VEtvh6EDBUsUk66y8/erm9TY2A5NAgUOAFC29sW8XnubtkD7UGheuC3NYe+ACX3zAAAADlBnz5FETzfgcr6UK161fjdbXl6k8H/DlXPiuIwC6b9ntAFIpHPdd+z6GlVWgcCRI2u8oYOOGNhC4AAAAAlAZ9fakR/h60AW7YGdQtuoFCe7a0uOvlc1NbK2CvlDazwoRCj+QAAAEFBm0FJqEFomUwI3/vDoqVTkBH89pCmTZspbW1QWm3JfZZ0UQobs65Mf5LlrQRMt+qH8hg6HnjvEpO2ssyZ0Y2DKAAAAFRBm2NJ4QpSZTBREsb/+8HrVh2DcyHMmT3dUQJoZsKjKvT90DkkLfs6f0DlI9Vo6Noy8G4U70dLwbnSCcJHA+s7ZIbEbAz5i2KHdv5BjXfmYaICxMEAAAAYAZ+CakR/hNQrQmu509OcsGZ40siB/cfgAAAAT0GbhknhDomUwI3/++A44Hc4/O2JB6m19DmjiM44bKO1947whNbBWqUtCS8/5jm7dHXzg/GKHpWAQzxNsp/1CPUSWvt3lZLPoW2mRgyciF8AAAAtQZ+kRRU834HOcQ6HnuhEEjNaVKp2xQF7WlSmlAM2ebIQcM+HcEqhiiFkUxiZAAAAIAGfxWpEf4EHlcUhXPIKbaQkC9FdA677TaPsDtJppmm7AAAAa0GbykmoQWiZTAjf+8HDrHeCOtIL6oV0eH0f5bCxgOtFfLX4aVpxgeHP3XvPB/PFVi0PRBD1cFMejAR7FtsN/v1VPGtv4Q5R6r3IoLYn6BFezGJqahiRYmgCTMTFmr0X4+/IlITNyh6ZhGBjAAAAS0Gf6EURLJ99PcOlKzJo8mgZiaFQ/cRdc7LruQ9PEd+7B2bhHdjrU2WOtGZMYKSDh35QT0pidHubWQAg3xDn8inXd71eAkmkAv1mZQAAADMBngd0RH+HoC9/1Oz3bWZ3fidkWX4f6J+c3kNLc5E/BUMQnT52L5poTTPp/CeS+I7NQNgAAAAqAZ4JakR/hnW4Mf925IfVrMHCbM+zJMcVzMO8153EB9P9wj2YcBm3d5ATAAABFUGaDEmoQWyZTBRMb8Em7juH6SagJhLTIoH9PuCUpPS4Xhl1dciUnk7fRxj9of4QGmTPb0WIBMBKQd3a1O1Yc1N/4kQ5x2J4nCSJXbRNmnX5rUdd/QcMYbapL5XWBGEC8Ck5/94+EirqJFZDiSW1qDre4E/cm0Cn57dgTQx4ezV9WnDqeA8qNeZT1cxuSXLNrFWIMllZp/93Xxk4j6Boviamv91IgB12vJyIkQkUqR+e7+pn8A3J1nM0FWyTrzkO7GJqjke9Z+zeEFS+E9FCk1HBH1Z9GwPLuDpVRLSp8hvtMrExkYwEqFJ7b8rI62OQzrSoZT3+EHwb1gJEd+fqytM5WmU4uf9eEg5WPacy8CyFQCjn58sAAAAdAZ4rakR/3Cdw0sHnaByzNI/DvCcZ5/JboTOZcBgAAAC3QZotSeEKUmUwI3/IDapfcEaIOp2yhhwVS+etbAWw+lDUW+XfaSyWSVIb9I+3RUZO9EZN/RG2xvytqz5YiVBaQOmzJiMDTSFB1apCB91XRO3SZTj1OyHGJzMKmR0UueNYpWGgXCDTBd912jNrkePuUoCV/pwa+tGQblTlC/7Mntowg9/HoHjvG3CnAA6FIBd1q7bFjLrs2BRVwa6HPk3/SE5LLvUOVOCcaqjJwOK9HolGgmBx/uLZAAAAa0GaUEnhDomUwI3/+SpVI7R3snQyETVUSNJhOfrvBOYWFqawEPOShEdffbNNhj/UU5hbs9Q5x/bRP+L8yTojPljMi35wxQSoHRofUyY5upTIiGlJUlsx7SzyXtNALaPJF5KQsK7ukuM7gVr7AAAAMEGebkURPN+Wev9EAG9OqVVseJN+Srxy0ekOaNhA4RLaQQxTYVhStfIpPBuX4Jw3IQAAACwBno9qRH+BB5XFIVzyCkOiEfbn4C8Yo2klpwwoQ9Hc0Mg62lIXOy4KcuI5ZAAAAKlBmpRJqEFomUwI38epbng7UbhTYq8oWxUQirssnQMSb7P6LX7rQapmaLnTKX/YQ+mvBQNdrilXpDana8P/ipoHFw1c/59iIYR8R4DQJ3J7cU7mE0EUO/5HJiOmapC64SNTZLeKkcj/wYvW9CxQjNGOHbzdRsVXG5vsdQcdxC46I2UJ+EPeA96UgnLDLarb/ye4UGev0CmekJlKpnTVesJsW30eCABpwrUgAAAAPUGeskURLJ/TvhMx1yLmtTPxEmDZ/zbA0Szadw/YOlgT6D9Dej7fdLCUUYjrPMIemsVWLNdcv1K6VaGlVIEAAAAvAZ7RdER/h6Avf/WMAZTJgkbiUU2GBEoThRTgMuuNR0WaohOWxAZ8OhULFKorD9YAAAAqAZ7TakR/3Ck9CeH/dWEJcswcJsz6v+24f6f0uV9+xslbm5g70jXAFOIQAAAAWEGa1kmoQWyZTBRMb/wIE8MgEocd6yv5CokI+/G0GUpxgPsND4Bh5ENvi1KT8dhbDutyYfbbj0VildYU+yRxq6FTnj94kjyaTRWbO1POXfK9v3D656X4I0kAAAAtAZ71akR/gQeVg87SZ4hSEgBWkj+ZBwvgo+Jen45iep+n2pmujJBvuAKIzNpAAAAAW0Ga90nhClJlMCN/++FR2BweCG1oRIssFvf41wc63i/NokGGJ2DfzyRKo+/2yaVmu/JJW0gloXeQ5hZa5thWPWMobrf/C7m5IKYTrfpZbz3D6/1t8557AoqeOYEAAABKQZsaSeEOiZTAjf/74D1EHaAmN4EWir8LxdLPbAZPO0M2+PY2QoG/X+gqbY2OfphX+3ikmCHy9qBRinr117Pf3bBw8DsA034PWVEAAAAvQZ84RRE8331NqbcOYV8ovUPdy4IB1YpDWJQ+wSvnfZOYkxxmYn/OzBmtYmp2A4AAAAAeAZ9ZakR/gQeVxSFc8i68ngUmW/lZdC6KspEQb2WPAAAATEGbXEmoQWiZTBTxv/vD7cBveCN3NVCXGk0YJpTWI6S27wxEVPlc6/1wnjM+kCdJnnhPo14+G+H7r7GYvUMBsla4e7HrvMr8M+H/u8gAAAAvAZ97akR/h6Avf+N3Bj9G4GRPrOabGjKMwWGQh7C506fOxYn6ZCu0xmofqwUIi4EAAABTQZt+SeEKUmUwUsb/+8WeTlkZAQajP8sPFAhMqTrywyxwavokyr3SLrM+i39/I0saTn/n2QD6z2f0Tpf5o4KXxn3mPA9D17rJX36vYMwHwHjQhLEAAAAoAZ+dakR/h60oZYAy4TexgRX9qffNaY7PWMw/K4c8McCTpcC8j63SIAAAADlBm59J4Q6JlMCN/8oIQcmggO56mz4fWIMz/u1LoEQTzV5bZxKWzyYuNw0VUour5rIsDiU90umHFkAAAABPQZugSeEPJlMCN//74VHYPx3QaH7LO6y4C4GizeyIGkLKZnJtuowmRMNw+Y2sUwAe7deUwGTWQ/Q5vriZGTkIsv3ToEeu8MQHLbiiuEa7gQAAAGdBm8NJ4Q8mUwI3//wC1hCBKrZCFEPTQr0tZ5jK1ORrDiuY02WzVUIDXNEWNY+1UnXo/FFfdn9MboNzrW4gnI9f6iOOt0mZ3nsie9H+/u+vO5CY8Fj2uueEyKX8U3QhLU3/TcKrIh9gAAAAHUGf4UURPN+B+FaPyFFuXZbgYj0u945+bqtESVlZAAAAKwGeAmpEf4i9r/hMofsoVdV7ULdGYi0qwviD0eMbDVTnrmwEwqTMGF7q03gAAAAxQZoESahBaJlMCN/74DjgiVvp1xngahySXAFM70j0RNfE5vodU8e8SsZ/26Vt1OWDKQAAAEhBmiZJ4QpSZTBREsb/++5XDgeRcDFSaseJcUAQLJ6v6GMzRDVOjaHtSG54x4li8aIVzsEHeZQB/Ro7xKkMpXenDvr+1lP2ymEAAAAvAZ5FakR/inEMYhyP/rt01Hns1oE+sW1Ax0NveAZCmuIIkSUdWkHaVhqEgSKY0rkAAABIQZpJSeEOiZTAjf/KCEPOyTOHP3Ga0L1Cjd69G+Yy89VyBy1dtUAy8MuVk4n3KI+TABbfvtiT/pPhtktWSGJDgIe68yWWzEpvAAAAHkGeZ0UVPN99TaObeIE6IhnjGIo8WCgt9DhhPUr/BAAAAC4BnohqRH+HsRJVvmNKEZfxWUIT/qJm6pPNGuzqg2ax/lk4U+jw9QOaXPUFfo64AAAAj0Gai0moQWiZTBTxv/1jexkZY1WGJiT87bz5jKDKBIPIUpqiOKoFqMU9xuIH4219MWUk+QJUsSOay780Yn710gUCq/8q5pJ2Qna2z31AIaW/7r/7nw3VuvmUI84z2bsgf+2HLYAKweSx9T91NAbzXY1OYhhH/Rhoq7G9wO4NOvgpiqkSQhrt64wDHDxeNgBhAAAAGwGeqmpEf5BXF/8DaTeNmUt5+Es7Qo54X0Q20AAAAGVBmq5J4QpSZTAjf/1jexZqwShLchuDwYT5GB8R7EoFbWKry1qGHy2qItJHoYSzhYHpftREHwxHK/JzKBYIGEaS6NFr4w6720GZ4EwFtOtZcCzxJ8lwWGRhMcXbe390w6J20G6KwAAAADRBnsxFNEzfiNYJXadE4qFM8TYpcvRqNU9B/tg74vA6eRce+4rX2wUXpty0yO3D1hzy0b//AAAAJAGe7WpEf4EHlcUhXPIKbzh/PPJ8bNtdgqDOQTgASBJuMgwAoQAAAPtBmvJJqEFomUwI38HgcMuPufQ+1zSgVSxP44j8Xw0s65lEdmNlEJPiZc5zpqLY2ASG2JgZLJLCVojwLtyVBJ2Djl4lz8/HuoMrqAhvSAdjKQWYwWvku/PylYEHSBWY+TA2i9oIGaNmjGx6LG1Kj+E8emazrbQW4uXowHf6FzN0wi4FUL7pYGixlMvdCCV20iEhjFpxLFL7zMrQiJXs0FePfh470wQo3VD0x5R78tx3albjYGEvRrqiobpY8RLCYk5k9JRliWcwQKwaEOM4PxOill4WkG8tUs4AocgZR6pVN8VW+TjxJGdBFTt3zP5oF1jV/E0tbkUBVpsIQwAAADRBnxBFESyfzVx+Y64rGY1MwatWM/mzoG1t/TCEbE6JSTBCJehjrlgVbP29VWiv1os6uGraAAAAcwGfL3REf9Pc6p27yHwywlkNj5DchvZ7/p56d3iHqPJJy0TZlJpjk/4Pz69n5WmRBfZn9bTbyeeOj4mF9//0Kaye4vxJwYkk6gt36PZDZ4v1fnhhsDUliUbwZt3CzWRiFHU6yepOVv1u7QQgijOhrLbxJsAAAAAmAZ8xakR/h+Qj8Y/7ryVtMCNL7ETyKPTlzq4JQ5ZB/FO0LOIakpEAAABFQZszSahBbJlMCN/zu7OUTa9XLb3eW75wn7Kv236GCYDyaFkEi5prdIBVAhBOujVHZ91Dr3M5z7YWFsFIPaTIAQW6IvvAAAAAcEGbVUnhClJlMFFSxv/8EDLdkB8H1mLAmhRVDhMcY2mJZi7xKwfk8PJDVJGGU0diyt/1T05IeVjQbyr4y3cfABc+R55miDu8jv+ke/z5Wlghc6LtRzgp8YEXwp4pBzCx8KkBF/UR9Q5Fhyu0T3/n7sAAAAAXAZ90akR/iOA7CrYE++zmxFeP15r8ZlkAAACYQZt5SeEOiZTAjf/HqZ9anCKYumtLnkGnytBtxceH+Tab7WDld8HDYn4TZQTl+0UF3pmdah258gjA7l14kM0QSfHRYvwKPR5YYx3eazaokeo5MLOsjv1A7a/dr6fcvO8RrTfuoTK4JG2FPtdS+FD/W7GxeAJSunV30h3c7FLYa2Yf/MWZdi1pjZRZxjRsrX+SukflJqQC/GIAAAA2QZ+XRRU8n6Z5caH3IhlDPEGgwBap5BKO3zzvTuKfuLPQfl8w4HkfPygPVlVEzHop4sE0RruBAAAAJwGftnREf4Bxg3qaOZGuOib2Lt1Wma/ikk81jKfwGZSD4w4Dnqo5LQAAAB4Bn7hqRH/N5HsNDLGJUxpLAK9mT9gX6FWVFLp4DnAAAABhQZu7SahBaJlMFPG/8pGuAUUgcuCRH/OwAPFSZJ3zCRS17ZFc1C9yqHmc8/pVK7IEw/oV7yJdMcMIavNsCKHsF1NBZJBQvHr63RBRk+tra9PDpuRrNWg1DBO3hQlugGNbeQAAAC0Bn9pqRH+BEcGOCVJY/QJGN/iSBG3l/ieze7n+BOFxVwCwANjCJKx4MnZIv8AAAAA/QZvdSeEKUmUwUsb/++A44IUf87s3whHef/K9eDWAMU8ldp4l040VzlBC65n7nr79QcRWreur7WcWsMhEQu2fAAAAKgGf/GpEf4l71o92pOxqkP3CQfV7f8+wwfDv0Vwj8bTm1R7NQsii5g4zwQAAAGJBm/9J4Q6JlMFExv/74VHYQpILXs6KXOmEj6xleFO7YS9ET5DIDmczDCW5+M6UZA4DyHmCTz5FoaK0i4R5P7y599cypfH8m6UCrK8Bf/vgLazNz1wC/16tdStMHxBIjZ4P1AAAABkBnh5qRH+I4DsKsOugF12qomW/SycjPJtcAAAAZEGaAUnhDyZTBTxv++A44DvMfnbE2rCH23XL0djoBAvFeci3xno+OtABO36XM5KyvnpfzxKvle/8J3308zrG6JVwhHh4rsYQUFRysfHizQb86GKutGNCt+OY9sPNteGFKlv4dXcAAAAqAZ4gakR/h7ETcb56JL659u40VH/5IqeKyAkelvuqVBBk49Saqe7VmZuAAAAAMEGaIknhDyZTAjf/++AcgTWtokgude4jcmomud9G6Ir5iVSH+0BGgJZK3LZX0JscoQAAAFJBmkNJ4Q8mUwI3//vD7l3aeLA78Ouck9tQPwA8lswErUECEWb2BfP9eCGIuUSrHm2tubC/IOXP44SUuC0wRBPcOUZ6UwEfr45QL2uWpkMDOFPAAAAAbEGaZknhDyZTAjf/+8PuSNeECUbzFqDoHVeVghCqcIHbUioPt8t0kMbXawr+fm53In7aCpqLDhgohy3fxI7sPPRi+v4k4BfCt+ArbIn/7hr/fNgQ82kiJyjRCR88G6lkZxT3zjOqC80cK2gQDwAAADlBnoRFETzfhHJ+XgcrH6mSiINjyp//kgke8KUWO3XO8bzOwBXQN1PZPssKbb2Qy1CNfZzQq7z2F0EAAAAsAZ6lakR/h60AXDOWhD5PePsAKefz0nC6cwFfCc9G9GK1nNdoyU44ymIpUb8AAABDQZqnSahBaJlMCN/7w6grIDgahJq1KP+AGMxDG43cWoTqfOrO+8pS10UHq7xCbX4a4pJt454b+M0H1Eo62OjD2EdzxwAAAFVBmslJ4QpSZTBREsb/ygqpP0TAwFGuhMe0H+LKq01NhOFINpQwf2KjhEvSCqmG9OMZE2/Qqy0272Gt0nHLi11coL4E4EoCisDk2gIBnTbHmELqhvwwAAAAGAGe6GpEf4k2H5XFXBQn5jeU7JdWsFUrSQAAAGBBmu1J4Q6JlMCN//vBm7tPgAJMRPqV++IZbHFD8LyLSaFG1Fj3xnwOAwhoQ0X3rcSTj34Hin1WxPUwQOdGW/+/l7yZtILXAd5Z9Wgqf/aUbVivhneizf3wbIgcVChpYYEAAABCQZ8LRRU8n31uIe20/kHBd7snCnkEo5gkxddxXoKnJ5QnndNiDb5+E/ApR6QfVh6OvB748Hzqtcox/cwTiLlMZwRAAAAAKwGfKnREf4ew+Vu++ZOvbsHGmKLhf3qdqHvTmJsewon5VlSMTQ0CLsoGQswAAAAgAZ8sakR/hNQrQyxiUcxzYxkWBaHD9jMxUSoH24CdrYEAAABJQZsvSahBaJlMFPG//AgTxcoiqckUP9wRELdF44LQ+J6zoeYQmvRkGAschCcjUxhb6c6E9RwD3q5AdXgn/otCYxPZzTf5u3+JwQAAADEBn05qRH+BB40VhOFmnLMmN/Rt6KeYRcltPwhBw6Yj8gKW5Be+d4emYrBg0QKVVh+BAAAAOUGbUUnhClJlMFLG//vgOOBCV69hkAISts4hjQjQR4n/taVosJ353hMn/bQatLAc7vxjVVNjpBAL4AAAAC8Bn3BqRH+HoDXutdsAXaf9vPXmz+vq9nHMZG8uHAniuS2VQVH0wdR1pyOJp2ZkQAAAAOVBm3NJ4Q6JlMFExv/BURmgKmQ2sem8M/cS5YiFuDEKj58ESA2Ioo541n9schk3ne2Kz27RHR2Mtl7sfZ6Tk8CR5fodz+unZYWDbUKNmf3Gr1+o9Rxy9lYH9eJLD5VCN+c3svum1sTzNtsJGBK1cHItHWUqHplu24Ckc7vqxHOe0ZtkSJdUThqkuknVrcKcY1ewS9SB7XN0a/r2y0t00YwIYLkLxxLwPmaULQNo/ape3s5adm82wYXnY+bUcZi6E22itPWO92rOH7IP21AdVJhrsPeiqRAczmuYnL6vkPzP4adcC7BhAAAAWAGfkmpEf84wSggSH8mtdkNj5CRqWoGie3lqD7CWgy3dy9dUCO//W9YZNfE6Xiu5GIKYFfDcUA0T2ZBvdwLfngiXt+EDkfDWv0K7BOXReZCjX9nSExoLgSoAAABZQZuXSeEPJlMCN//74DjgOprY+5Fmn26ag8A76dO6JZsciGMnQ2pYcNEnea2hGeomPmCENu5nJeGhNjlWP/GqpX7N4n3KhFSvZLLumw3qoPc7g3O8DwTNGcAAAAA4QZ+1RRE8n309hYNYZQ7+HfhECBi/K0X4frDrq0X/KbwmZ8BThyQ2+fhu0YmOJCJCHAKCHj32xcEAAAAlAZ/UdER/h7D5Wmcz5GDrLvRDAsCvzGdqHHvdNLLc9bD46lxTMAAAAB0Bn9ZqRH+E1CtQOK5zZksA34p0LQK+zTMJUM7eYQAAADxBm9lJqEFomUwU8b/KCEPdL34SY/510oR6jdtPc2spsXUY6gpzzsPQOWCHtIKBf9RsoPGI0GagH7nZSdUAAAAlAZ/4akR/h7DkMUOCGBJh+lP5pSg5ck9EFnrQLuSi5xBprxkDQAAABQJliIIACf++DLeYgN9XYj5zg5B5QJuOQipq5DurWEjnW3UorRPL+Gmp7W/eIApq4hA6GO7MXHIyDHsVucz/aMFYtKTDM8z25ZLQfiNZJqSiDN9723ESzhz79Cwt5M+8vOWuuGhG+Tvq9foXjrYoVXiVDwEsVfUmlk5TXbEqyYI+MZpo4mDJdCI6FifaFbOBypE2MZf5ZLIBH7g8U1rXJzlzobXWQwBVEZ3H50m7+bGCm/s/TO02Npz6eSKcRnoIL6uejcV8scEPRiZKvQslaZioRDQNI3E8UN6+/c9cAekW3/GkyyVmNiyROqcKtiIhBHi7pAk2+Q/TQP/YwlHw0o4KIS19ytAyX2tYiRc1FzBcAlpBmEdwA9/TY2cHx8Zwfg5om1WDRf7F7wfrHwkDprLxXYIXoXvv2/us1OW+b+Xd99mIui98EnMS3BL3wkn/NUHomZCIfEFUCZVjLemRQTcW2M7/WlzDgz8mCOUhc7zw+tq0JUXSMkS1L/cOl+y1DmBMgMzcbhhNbzDLeKWkrUH5XaHyryAkCyr9U1Xo/mAgTLsnExuVa/P4R7zZdXvaMIk7ek35j2b1yb5zOOXYieE3ARP5O5ZjAZBq4ndpSi6jkftKCBVFGUAJHA0yJ3SOwTwLlD33y2ioFahycU0S2U1an8v7S5ljSJFgUt4xqxRzXPLMm5Q/lf3uq1K+w5f2w1oQlGAPQWlYXk6xSti7jEz02BQEwIn/1VSW1lDXiIsLQ9/68Lmyq7+KqScF8M2ttOd6ikUYm63rAEQ/1uwF2tw+vDoK58ZLDjGGY+DsvOCVmKReWQXo2mJU3Z/+dukASHHoLCtd6cudmKCocwPIOFIrZIpuWFPYP7N5w3TQNI2J7uOoaE0UlH0FyMvlyJKCSxK2KXdZbEjdWLzEYIhD/rSHAXZ83AHb4RVPmbBpaD4FR9EhjzYQ5kM67XQ4jgiM+83E3jJ1tuOTwVjEUXOTiZCx8wy+C0dX3lqduBIJOztDCxzLLUgy1pDJ11agmN4hiYEyREcCB9PLz7Gu5NIl+dxSpct15aKEYrYOog2sW/3UjxYhz6/+6vpRxNYFBe2YVFfEse/JAPrCdAPUQXwCIpcGvCAqIe9H6yayOc2/eUy00kYB9Hqrr4iHiednpjGYIa/wBQn2kawkd5eYwvCd09N//TBsRiPySS7TyptSNcNkl3kSijqYAzDU0cTjAlo7gSKsBb2500bskV+Fa6qC7WYdX+uHeAkkUfK7djG3sDH96VRFsoJGnc9eiCjPYyGt4n1nklz/pm47DKZfzdVcEQVVqXfQk182IfGwqQ6YPNZNc85BxPoa5UG0Nc7aBMBm/EEqGdEA+1bpcQALD7p+Xw3OdXvS2i01L2TZwR76H4FwPsv/kg5K4Q+gtukTkAv8aXBY908KoaNfjcQLsJI9nUsxo8CIfcaHaV3fe+aAg0mPXJT7WoUH3EqFX9KWMCffuLgk141zvZdeWb9G2OAayn61mciuEFWUB3unf//3UT9ZWH3tY8Odtz5knWw9WcA/7IUpQHs7AGXSZHFSuMbAJ7IeW5YgcJ4/2xVyVfqWwGS8VXHS84Y1Oc2iGWIYRH4DJUk2xI3a7Xnxr04yGYzMm4F14EhpEJRU19w2fQAabSuyr93+75ace0n91G5DJsmYh8MQGBLpKZioZADEdyOWOL/b3dH1hKiEdZsQsnfs/yhdAAqZAAAAPEGaIWxG//vtcWB39W5QeWKwGeLr7PMDikgXstlWyUqYOYjnV7wDPmyxfP1O0kL6PoxR7zo1Gkb+C4TG+AAAALJBmkM8IZMphG/Hqe1RFY5rt5egm2nn3JQcBwuaJRr84/ddLi9/76agb7ejonTAqd67XVYfnqN4BuUaC+fAk+1lcholPMzUb84TknMEw30GkmM7GgprsKqCgwcaNTAOWdS32AuH1x/E2QaetB+/mEk87Hq8PlJqqlYpWPshK+oBJK6KuTSGIwg/9Bv+LVgT1pALr72MIHMQXmCPnTdlDJUk4bEuuLIqk2to3LjxIlmPkYtBAAAAGAGeYmpEf830n5JzFkX3j1Qd8Q+AUGDVgQAAAEhBmmdJ4Q8mUwI3//vBm7tPFgd78v7l86Um29hR9xxADhgMD9LzlgdAOtozhLW7UEkoFYf5otHinj6odtZRtbm/yoQHLKnpnZ8AAAAzQZ6FRRE8n31uIfah+VmyyuHZOFPIEoZWWjwctTHilRP/EBaLGght8/KC14RVIcYtmzmgAAAAJwGepHREf4i5UoIN1KmuYE1Jdm1Hq0jw330ncp4Hx2M/vKMq5Z4tgQAAAB0BnqZqRH+E1CtVyjEqWYFcr+nHuyCeliO2EXDZLgAAAE9BmqhJqEFomUwI3/wDoisgI4RECc7scuWX932y6q/Gcps10izFusxOOvMniGaXl24boq3k+UZg8rPMUT3iQxdNcNfEd4A2W68ovwz4f+7zAAAAVkGayknhClJlMFESxv/7w+5IrxwHtyq44n5qzWH5tokp7F/MIurN4zLf3qsBy/LP1c33i4OFn3CMZ1TBLIwXvbEHj14Vcb6Wt30UGVr0GhXroArD8I7AAAAAJAGe6WpEf4etAFVTbNo8fLWftyKUew1Jo8zvS8XWIpeI7UlrUQAAAElBmutJ4Q6JlMCN//vE9pkb6iHbDeYYscsOWTLED8gfFsN2l+iXqUwnGAUp7SGWRWf+Tj1MgMpeHoo49mO3nGzGBc/JZpGbbwjNAAAAM0GbDEnhDyZTAjf/++A44EO2R9S7lNO7TCCSBTAoqxeSZe0BGgDXxryEEgFwFd7IWXy8YQAAAFVBmy1J4Q8mUwI3//vhUdg9fr4QWmZ38DJvZypVj9XjHyiWE0qfucSUpcKpbBOOJb2lRZHjFXqbLB/AAyfZJCzDiBXSwSe2d3nKLM/QAOEJb8mw3ifBAAAAT0GbT0nhDyZTBRE8b8oIQ86YuCDOQEpyy9UWaSGWOxDO7wiSCJDYIhWcih6YPXwwChxsQuyPkj7FvLL/pgLFAoSeOsn9+9F+L3cY6vDMfiAAAAAqAZ9uakR/gQ9o5WBUzd28w1hPr/+56V66bgLLEK74aalrWqpQrcrjTCoOAAAAM0GbcEnhDyZTAjf/++A44Ilb6pCfLUOd7CmgqVP5gaCrD6c4HElsj+qTov37vq7C0KSjKQAAAEZBm5JJ4Q8mUwURPG/8A6IrIENjLKcjAdSvTZ0AUa2HabW/T6JywFoZYZ38XBzMVTWT9Boz5CqJWPYr9zgIZJzhObYT/U75AAAALwGfsWpEf4pxDGIf1f+tYtz0i7E23tkVLGF0sTK0M/3IyamfbQgfSCSb1BPWNUB8AAAAPkGbtUnhDyZTAjf//CJ/zIEGcd9wsgkXNNOYwvx4RPo8KfLe4zM2exTFz5+cPtcVtktZUvjRO+buNFeyS0d/AAAAHUGf00URPN99TaObeEbxmT4L02e3ocfhCFkUcu0BAAAAMAGf9GpEf4exElW+Y0rm0PJ/wJM/5jtBqG7M53mIMo8kEOBG1R7FTsO3uy/8wE9rgQAAAIJBm/dJqEFomUwU8b/74VHYNYZ3gTviajBNW4911XFIgnHuPPvf6PuINmTDgKw6tl4y1gHxr/o1Eoc443t4pg43rfeXg457+frQjkKJwwuU0Dg28hgOuxXzoy3/7GNwWDiho5fxpVul4JYlW/s2wZ6+FIFz9RB3Pxla/HA9ajISXcDsAAAAGQGeFmpEf4eNe2TaTV+is9Z+r3dj/4M5IrQAAAEtQZobSeEKUmUwI3/OMDPDLD7+9LV1rvQO8dIyBgu6gBZzKIHOqwdcYW0mj4/p+KVMgoNINR6fpQkbB+e7J43ZN6Thfy8C35cIcvE6ulfkdYr0ZP5p+0EdUB5C7vhx0r6X9DkZA0F/t8XcJ1Ay1jkXzIafX5I9JMZyOPPT23eliytMDQN3Nmm15DczfsII5lelhwcySIf4ku6wzRULjnVZWRMxcaOWnCVmvPK0WKnXS6hAGrBovrD5c40CFPHBrLy9JtUVvwuELo3rnQUyLyChZip9Wl/I5F161BBhavdM3/GZPS1ybPzPYbRmbIdt1SZq/ybmP8py+DhwJs6/zeOqKGDQvWpP3NX7JQuvuIBzABOHx7UbDGk+BICall/zPVb7YjEOQFeJndkK8GmaDQAAAEdBnjlFNEyf079+wBmd8pEhgLG8Xc7r/4rqUnzQQEv0ohGUAL7kLhkvedlsNVqrHka/nxHyyL78FF+w3UmoM16jfBFXZPl4yAAAACoBnlh0RH+AbCj0Y5cPjkp7DicIuUO2PQpkoPz3B9y9RBSHaE7ieceKLYAAAAAtAZ5aakR/3CsoGURp1Gbf6xetbBvbdg7XQAomNjTNFLLYoSnN1T9WTArX1a5xAAAAf0GaXUmoQWiZTBTxv8gax37l9sNbrPn6iWwUf8RxgPTzKiAAtM7FGqChNHUkeqgcz5deTS6OtdOTIhcU10sqfJ/G7PWcIpym3Fr6VN/qN2kQwEQKlPOHuE2NC5vUo2j48TIVNP/sgwP3kyf759nBR/KFqquMTQDl4oofPV/0HkAAAAAtAZ58akR/qMvbvw4JUlkDvjD/f4ktFYfXNpM4bGfm2nM37h+CiGSGIelwUC+BAAAAQ0Gaf0nhClJlMFLG//kqHGZpISriFH/O7LMfxPF3levzQUoyR8IdbCT4Om5XQ61r7CJghz72IBV8PYi+fqlXXR+phCsAAAAuAZ6eakR/iXvWj3al4qv+G0JMwV4sy5KJ/S3ER7xINYVeI2qPYr2GLSVtouwGmAAAAGBBmoFJ4Q6JlMFExv/9fMnXUwOYhp70FTBXdRxtKOUP+0QmwlUd3WHIaIY4UFj8Yp5d9xxsyZ9jD2OI0QWf2S1Zcu0qWkg1dQDmX7aUzQyVEEdscleAHvns65gIbvQxIesAAAAWAZ6gakR/h417ZNpNX6Kz2wcSbK+6JgAAADRBmqRJ4Q8mUwI3/8oIQ86YuCDOQ6JMWiYf/iRlpY+LK2B96JBM88YxxpmUYIE8NLeGUY5dAAAAK0GewkURPN+D+9PfvvPTrHvZhI32OEWXdhkGCruwTO5SkxQve5GtKHjiB1sAAAAdAZ7jakR/gQeVxSFc8gptpCQL0Wxcfx1JTwVzldcAAACEQZroSahBaJlMCN/HrvlPtuKGl8J03skzn9tX3N+UGKgPtt5f1yW1mhtqYtpj2nU5x+dydduAMrmraSE5zwuU/zBlhXh5g8x8c38YYs2MriPKi55w4mf0Kwy5yX9U4VJwu6z5gBqEnviXVdXlHGiM76Z3+hcdzSXdSABOlyYkF+z/YctAAAAAM0GfBkURLJ/NXH5jrirbiX21Z3CtP5YcQMlKXKnVSfznQBXI1IR3YSzO0uaD7W46BIwddQAAACsBnyV0RH+vXBhX1AzTfh+ibFtqz6eGMQ/DTS0BafSOlfYuavjTxTNGlR1QAAAAIgGfJ2pEf4Z1ktqM7YEw3wBj96oC8se5g/yL18ispByD6DEAAAA3QZsqSahBbJlMFExv87zR2otPwnC4OQ4HEYtHszJf56ewlbVNYDdRLi7DaNM07knXvZOBJlAsDQAAABoBn0lqRH+BB5WDzvLCGv4OSr3ZyhW71ibSYQAAAEpBm0tJ4QpSZTAjf/vhUdhErrGwgYAPXwOxHLcvgTv8A/Zx9DjubfQaVDTVMebAKQ+RkqueYnX184COJ7NhYucgTvasYPFK4F93kQAAAFRBm21J4Q6JlMFNExv/++A44DvMfnbRI+5Ux9vnvb9fZlh0+yV0sGFmgEwKg3swNSCp7N5faAgb86AHOGr8Z8DiL5UcrnP4kp/lO962eXNLUHqjhHcAAAAmAZ+MakR/h7ETb+sxp7t4uJPRUf/kip4odeYTuzhEga5MwAislMEAAAAyQZuOSeEPJlMCN//74DjgQ+tokMdI2I2w4XB31uWN9xpSf/b7WfaAjQAslbjZLMjZGYAAAABOQZuvSeEPJlMCN//7w+5d2niwexjKrUXs6RzXh1b13XA1usrXuo4Q+o9ouvQ1D2Q2Wm0PpqZJ/HVh07u1x+848SMV9TQUPFzbtS13xBB6AAAAW0Gb0knhDyZTAjf/+8ParGvCBK2RNZj2l1x+jAR0apeuLZtFzhogEMtR9DC/p+fqK8dILLP+IbGFXeex19kT5IY4p8S3kRMYF4bXDgihVw55Ypd4XX0hDtrHn2EAAAA5QZ/wRRE834ISoy161qatNvHcx2Sov/bKprDXTckBV7PTdQhKXDndGlf3VqRlNangjqwTorzS3wjwAAAAJwGeEWpEf4etAFVTZCVL4SV8n9Kg6fE2aMda+98WXMvpKkUyXESP/AAAAElBmhNJqEFomUwI3/vDp1Hm8IcqtaXumQqIJt/IdZM/LaCqQDp5QDE0HJXXxYKzqroGplUEOXiTI56df1TOqn/+xhhrsArE1GyBAAAAX0GaNUnhClJlMFESxv/9Y3sbc/g1iqUD5e5Uke9bb1C6YGB9z3awZ4zQI0M0unHOdJnHA1rrnsh4SimxV5KpEdhAQ9gFOWZITFvHKgu1okFYOADb9R2Rozp0tWuEyuxNAAAAGwGeVGpEf5BbOR6mhp16rXzs2+e4d71TyW9KQQAAAGZBmlhJ4Q6JlMCN//vgOOB3OR7sTCb+I0045cZJEQ4U+L3XCpcnHOJwFb1e+Q0W3Q7uM0aQ6yv11+YTtxoOdsy7RQ+LYYJDbR6Rd7L5XFyxpKDB+0OWkFw+YLFioVUh95SYqxqecX8AAAA8QZ52RRU834P7bhmByo0v4xgbLCddxYxYbspCwfmTeE4h4m6gzc5yXll0xuqHmzfhwYk8ZpP+UzAb50WNAAAAIQGel2pEf4EHlcUhXPIKbaQj7c/ATAoSkel2SJNrDV4kgQAAAGxBmpxJqEFomUwI3/125QhMIg/aNp4kCmUKfLpf/QrlLIP5B/oHjyqjvBOioUOoMoL20buyufrF/UIt3sv6YVyWShlKuej/5mcBImU0X9mB2jkyIoDrWUZBjFA6haFPIB5+QhgWaDpI61usiRgAAABMQZ66RREsn32aKglvf2C9+Bdon/+X9WLOPlx7m1yaoyHmMFFCEI61yhs2ICjfrbx1xwg+UNHTSx3O+VuFL6oUO/x14wF/ouCv7NddvAAAADEBntl0RH+KcQxiH9X/rcK9KC9kRdt+HJ+G/iNZPTGZ+diB+2gxK2Dg5bXzZO90e7yZAAAAJwGe22pEf4Z1uDH/deStpiTEc9tyHnyaVWKk+d3zLNz5R2rfH3mWEAAAADpBmt1JqEFsmUwI3/wDwzHAd5jvuFkDKQEVOoZiZuzsM3wXcA8F3ucQwarCn13TkhsIYJMiT3kh+1+BAAABTEGa/0nhClJlMFFSxv/BJAf7z+EOU2CWqKManlZ0LpOsfJcBa0PMmIuvVH9dbvAcE5UzEdHVQbTrz1N1jb9WiMOxu5R2cy5A81OxB+5ZBnN5eAsC32urtdTTKK7QubeiVOo7VylTo1bcPy3DSeA7L7gshvKbqb6yZRCFyi4whKSJ7RPi0+A7uR9tDdfvNWqXsP4NZEdkTw7nJJscByribz64QmP/ICkD3ZxZqkoqOTVn6dlY+P0SHMy6N2fn26w0kDbCjyPfS1dZblvZxWjU616LYFDlf6NoIU40eHrlDbvSs4WEm5sqj6lAdmcaMt1R2DDkFv3lJfP04qVFUwKkb4diB0AgVCwqoCaqflufpSnzlUP5zpGT6XXwKzdvxhIiAGd/HKUkMg36acC0SDhcMpk2kz57Q3kYvM63o6ggTKO91qvRby/6+hO4M9+BAAAAWAGfHmpEf84wSggd11AiFSufQxM9Luw26hRYDDKsE9vSwAn7Z3kp0GuF0Eqpea5n5unP9KN0c3L3GkYNAsaXrACiTeM7/yz8z1LY9wjvbK9PNQeYtR6wMIAAAABfQZsDSeEOiZTAjf/zunZ7sMLNRD1X4hTb1ZFO384nK86xDwxI5Vpgs+jNIRsolIEfvY6lsaVpHcZvcrFP1//9wB89zGosviWcqEVLIZ7/+WmTYZWLoPL9M4Sq38lHkYEAAAAyQZ8hRRU8n3sgpbafys2Rudk4U/87NYQWWpO4rxUES/JyvoT/lfbQ9OleOWWIZeQm2IAAAAAoAZ9AdER/gHHeAx5KQ6o8Bkl+v/9P4zpRB6XdjaQVIDAbYhBhxO6UwQAAAB0Bn0JqRH+E1Cs20Vz506WBTrufbbvFjJQQkJ28wQAAAD5Bm0VJqEFomUwU8b/KCEPdL34SY/52bZ/rGfsXPf5yCTDido8G77Go9+K1BJRLL67CRLKDtmUDogd5/yp2dQAAACcBn2RqRH+BD26tG3o2sOTqJd//LOf/0exfYfKLxm7J3qgsoJYfg5gAAAAzQZtnSeEKUmUwUsb/++A44IUf87wFjEcFriJhVhBdOObtZMRA7H8L7O85Qi1Ir/23xQWBAAAALAGfhmpEf4exElWANW8vMvakG0lwp25Oh8MHyFQGujxVcqIRtUex9+Z6rsJYAAAAUUGbiUnhDomUwUTG//vhUdhCkgtD/OKNA6dG1osSc7TN7EVmJ8mHYKHJo0kuzmVJ2OcDMUMCsDRqChpG0X62F+dsmKA1zveQZSPh2EvbMNzsMQAAABUBn6hqRH+I4DsKsOugF12nJUDYjiAAAABoQZusSeEPJlMCN//07ziWU3i/kpvzHFCOn2CcItTi1yFM4xbCfhV3Hl7kKDI/9hW4d+LvO5FlZCITgCrsyrc2JS6CCKYTrtDuaRaAvGVwNn+giCSu/6I71EcdF6AAzs8YoRZvHfx+r8EAAAAuQZ/KRRE836foBN+l6dUqFIqLR7fdXGqeg/3ZJ/83TbhEn5pkA8K9kzYNkm5sAwAAAB0Bn+tqRH+BB5XFIVzyCm84fzzyfGIzavoEg2o1vQAAAFdBm+5JqEFomUwU8b/5u5inuMF7Xq8EOtQO4YzuHe2WcPkEeIzleyoHzUSRtXdZDfWefPY5FxPuebWlVj4LVrBtGv0RP79YCO7uGRp8no60SRyqygnK4c0AAAAxAZ4NakR/mJ3s34Q5H/1uFexTk3VIjFEo/fwfhqCP78QfAaOXWnodXpVWh/EbwDilHAAAAEtBmhBJ4QpSZTBSxv/8AoC0RB/Gq3N+gqfQl07//0asYn4GjHzgmabjVHeCeHimF9bt89uzqXPNoLLVfk5lK+SEWZBMH+jdIMD7NJYAAAApAZ4vakR/h+Qj8Y/7ryZBazBlCQSff9dCgZHsO8153DG/1Iu47qZ9y1EAAAA4QZoxSeEOiZTAjf/8A8O+HA7nHfcLIGMIzn/UI6nxZeLzvaZpNm4NR++zRTzqakzFTJjhdFc2sWYAAAAvQZpSSeEPJlMCN//74ByBNNlS+GOnj/iYE7uR3e+eaDjQFr415CBPX8resUSqg9AAAABEQZpzSeEPJlMCN//74VHYRK5/s8oAjSJlNV182JSgSuYDsNFFvPml8Z2TC06yUjgNGaIT+MGdO85RZn6ABwhLfk2G8T8AAABHQZqVSeEPJlMFETxv++A44Hc4/O2I/eN0x9t9al1x12ezWSFYh9FLhxu8aTceePsZOcOf/noBS072io579hr6GIKYfVDIPsEAAAArAZ60akR/gQ9vOA+UWsy1Mn3l//+f5lQBPWWuO+aJJugzYNwOMU04nd4nwQAAADtBmrZJ4Q8mUwI3//vhUdhCQoq5E2kM7tSSvPdRsTqN2Lqyx1YynzDOfYXeFZW5mbzBzov4IZCErRmMoAAAAElBmthJ4Q8mUwURPG/8A6IrIENlUdHg1vXava9lPZfzExXzXgqZrbgK0CpfLrF03F1J4CM9W6Uc/jlRwpOi+VZSpqhCt8/y775AAAAAKwGe92pEf4egL4A9rxjL4OGkdU1tP9jDyD8YL7cVkl98cgRXNXC0itKOXK8AAABMQZr8SeEPJlMCN//76z44Hc4/EYYtcgPBH9RgO0Iwi8OXMzKNpI9izDnv5+poRBie+02otvR+9Zng2UG2aLxEiEAQa2EyHRWuU4x0ZgAAADBBnxpFETyffSt6tLJT1q5nVhquTgXJSFs0fGTiWZMvJijPSmPEtnIL2xGLEmLILUAAAAAjAZ85dER/gw5gGJbwM9dZrSXxrWmk7SS7YSzCpHXtnb7SsiUAAAAXAZ87akR/gQeVg87QMSja2dC/csmeH6AAAABxQZsgSahBaJlMCN/74VHYPZq6tmtxLh1sxyPaSRfT8XGQqvC/Q4343DJxZXe1Sjk7F3JK8Ga+iJBjybN1BsuRRnEENt4FtlTjDMiyslD/QE2S+ie1BjE0ye1VekieYTcbeA5uWUCc9oAS0oHJc7DDprEAAAA9QZ9eRREsn3/Spxn+3+PADN8tr/k8dm4t2BxiP+1thpygNS5/1ulcsz7YQxXLmulOZ9bSD8leHIfywjbusQAAACQBn310RH+HvdOD1Xbhj4zYGbutZl5v6HqyOaqspaMKaK5qWeQAAAAwAZ9/akR/gQeVhtGJPhygpFSbn4CX0RYdxtJU+44KMHn0Oo8JLVH4iFlYBJyRuWktAAAAhUGbZEmoQWyZTAjfx83w+eo3udPz/wWGJzqAxttakOUQ5KIzV5rlta9UDHrdCKjDiNALKgEJA6puZDaqChxqlBxSo1lMmdEYH9x6+Iw/G7cBXYYH/vbSuijBJl7Iv1SdG2SxEz7WKOVaXQ7edDngcN7BTtsHxNL9kKKH2vdnV7h3Up4bBiUAAAA2QZ+CRRUsn81bxhrs9aLlJLIhMRyrlgrjTzbZxY1kexOB+js4nC73oqm2oS8KXxFXxDDK0y6hAAAAdQGfoXREf9PejGW7yeA3K+Ashsi9mc5rzdirkZ1i8F7plCYqBBB3h2i/Y5FflOyMyOlgUqYL/G9xIzGl8z+0IWKUH+x+Fv9B7/aREDydtwyQInEGxGayCrl3zIGiZ6TFkkOalquRg4SIEHzOpZAOwkut3sA6cQAAACYBn6NqRH+Gdbgx/3XkraYElmuoUqexzkujzvNedwvPC5NXjL0QwAAAADlBm6ZJqEFsmUwUTG/8A8MxwHeY77hY7su4Js5ePegnvwLeGgOmnRghK+XuEJ9p1ZqTpeEmvxKPv2gAAAAWAZ/FakR/gQeVg87QMT0JOabD8/BfTQAAAFtBm8dJ4QpSZTAjf/vhUdg9mx2D7TmdFz8qn2yy4xD/XK0YWq6bXtpAIDSMH89fnlAnLzoUK41nLcHaRGQ9VYKHwPVgSIejbid4zu0EWxJ7r5+L5ivAS0Wc7KiKAAAAU0Gb60nhDomUwI3/+8Gbu08WB3vy/AdqEakb767uJfNyZA4+pjSLzSTlkccggQWj3N8llt825rufA9wokyOuXzQUeglOEArfFlY1GPqmrtZTf6vhAAAAMUGeCUURPJ97IKW2n8rNmOXQIGJer6mMWBu7bBqazEhQPzV3PBt8Hffu6rEFJycnZ/AAAAAiAZ4odER/h7D5WmcyS/Um4Sed4v71PLKjq2xzxVZO257qOQAAABwBnipqRH+E1Cs20VzmQO2GD7g9FZUkcZEXqdpxAAAAR0GaLUmoQWiZTBTxv/wDwzHBCj/nXShHqN21NSADJU/Jvj4ZToB/1ZTomGKrdxyAf/iOIoo9DdUgNWCwuF0p6LBGjn9Ou8lBAAAAJgGeTGpEf4EHjJ+LiOn4U4Y39SmH/rJxyXApfMRHQp7a1U3JiFaDAAAAMkGaT0nhClJlMFLG//vgOOBCV69dVkGYj4aWcO7kQz+XVCeG9zDp5MQEvFDCbSD/y8jgAAAALQGebmpEf4n37kAo6ubDhZOY+8dpADGCpKbr5jcNPPpTe79afnKaDNgbJdD1QAAAAFpBmnFJ4Q6JlMFExv/7wetWHYG/jzvTdZyGtgH/DXdI0sh+SMrklDv0lY1O4TvP0MR8DIsygALU9Fs2WbnRqnA2PULv/h+6+vnPyCATdnYbIXw/4lIPbTxVj/MAAAAYAZ6QakR/iOA7CrB7i4FdAnsFNG/HYixgAAAAVkGak0nhDyZTBTxvyghDzpi4EXYDnbEfvG6Y+2zMQ/Hb9ovvg/0chC5MpSxOmJq9zFtkv9T+YO8D9ACgwbrIT3dU6sd/k0sYujXXO5r3U7FCF0bBVqsgAAAAJgGesmpEf4exE2/oQsw3fbuNFR/+SKnih1Q5+0sivYgWYAQIdsxBAAAAMkGatEnhDyZTAjf/++AcgTWtokgude4vE93Jud9I9whdUFDZJTfR+J6YiF8XdIzfNb/FAAAATEGa1UnhDyZTAjf/+8PuXdp4sDvx77lN0KgU/fpK8+IINR1opM0Pu4CozS8l0Ud/us9fntwO4IG0oZYvNBRI2N8a8g/0sIn+6f7QQekAAABZQZr4SeEPJlMCN//KCEPQpIQEI+hBvIFMoU+XSr3pPEb6tM+0KTmzi2hUR7j3nqTXSRaGGG+1IgCzYffvSdjj9k3eSImzEFiL89xBSAuBHuAan1qCm0SbL2AAAAA7QZ8WRRE834Ryfl4EPlupkoiD3UHAn/tyNF9cYKC+bqyZpWmU1htKRBn8v1v9le4Ox3FhOYCg5NB+ZdkAAAAsAZ83akR/hNda3bhOJUZIUUY2jVhVdrmxzmILX98ciz/mLhHdJSmfczV6U8EAAABFQZs5SahBaJlMCN/7w6grIDges6Q6N8LLKBBR/r+r01RScaGlerMw4mNRGZeM8kNv5hlT2J2qEHTq9XsVBx6y0AugLYMoAAAAZUGbW0nhClJlMFESxv/74VHYNZiODju5GRyMj9wlHqF1Lr7pV20VHiAMrHfGA6p0v/Zr0/dcP6yMHwgdbTTN5Pj6VJC/71UkDQHF2SgOkPs+SUevM06PMV4Dc/8UKpkKq5MzAfyAAAAAGgGfempEf4k2KJe5dxcZpOfMZDO0cRrAfBG/AAAAcUGbf0nhDomUwIv/+6w9u1GKETDvOv56IQEhONuu4MWW8U5koOsb6z0DMrcU8frYGb496r+0cR0j8/9YT3bwj6KnqNIzzKxPxz20387LsrGHGueynDGovxAkh6APLCz5JN0en8GfJGJgIpTpHDn4oF+AAAAAP0GfnUUVPJ99biH2ofqCeWdcOycKd3M3sDTTOXcd9oRK3Tz1jn02+G3H+x161bG+i7O4manzYReZ5zgb4/OHSQAAACoBn7x0RH+AbCaaY6JFZrmbJvY5TSCOfppisvf7jhPHXneiHxtzcLARd1EAAAAfAZ++akR/hNQrQyxiVLMCu/aBfsIZgv1fU2C7T3Ir9AAAAEZBm6FJqEFomUwU8b/8A8MxwQo/52bZ9Gsz+sY4O2RS3UuD8X0CNvCqI3uAP4dESewlMKkkQ0sYA5go9F7wlfSnLQu+tTWBAAAALQGfwGpEf4ew5DFDghAjpjv5rOLhqU4poz0tQCo2nvYk1MNk0SbxLDlMLbaRYgAAADhBm8NJ4QpSZTBSxf/730txLjWi6sL/8WkZ0SxdX+xgko4N3jmSClvhOh2sSC6GI34PiVLH5H6DQQAAAC4Bn+JqRH+HsRJVgDVvKzmQczvZIZd7ldG4fIdAbM1M+rxG1R7C8fxWQE6gkEOdAAABb0Gb5UnhDomUwUTG/2eshp/CHKaTLMVIv8yljz56U5Qg3GnKqoZs4y9mnt42jrRlYQiQvaz8jEhuvC1SoxRX3jdtVE7CW2y7mknEG5FfETEOLay9NAEvDLcXz3Kvp5o6r1Ecn5DKuRfL94ZwgC1vfOYsAR70mWBfVzPYmO5bWIXHrsivWz4TmSfJX8BTws++We/rWq+eEIZTOpQXO18Z4TBZJoJNh+NQKQ888eOePfvsvOd7ExGYc72AMj8IB5nA3qIIpZ26bWxdEigqp5WqA3CPmQ1ArglG8O23AW9a0mGskFVKIURyO7SDrMfvuSpM2Ufg2ko07dDeVY280dgvmukE3rlhobBVZ9eHnJpB/rDydTtHtcgUHxLRRN2WPaWUwfFExO2NlPxDNJNoifurDRR1TOE2xSpff831Zq9dXYJqU1zKACLUfsS7gTYxNNWcQ7fT37PoiXtJMLOR39uru4L0h58owlQKSE0dbvzeGUAAAABVAZ4EakV/6f4hhh5j4pezXGmc10booa3ca783iOnUPxnKk5Y3HfubpAwe+uDDln7x2eceSurPShjo5iSao0hBxT60iJArFLPcDKmG1k+1SrnGiBiThwAAAKNBmghJ4Q8mUwI3/2esg9XV1nn6OqvkurteiZEF/cX1g6vwMjx6DR+rv8oeaoyhuEGc4M80j7FCjt+C+gCKRU1EONaFpssJsG4r4wx6++Vl4OFVBDT83lw8NrDATf3vTsciLFGM8eGrhKs8pvOB4zbD2z/wbkYod2zVrCHakD3w/D1EFZvcK9tKs1Nr9e2ax3We6/gOaYcpR2bYNG1etHQuzTshAAAAMUGeJkURPN/smxOo9ZgcqNMHlkhDvC9wqzrKarKnUnQpTSCQZIsbYKJtUJkWtnazMvAAAAAlAZ5HakV/hwRZbYwm85/cCb2d75a9rssTV2GyXh2ERuMyRAZOQQAAAGpBmkxJqEFomUwI3/vEgWa+og3x1dnJ4PoDdBe3/3YoMTUoB3TyU4Ej/zJxdOfoW4aHRQ/I+nHKZhqGdhrBqoC886/vF7jEJplVhBx/pyxBeWOV/VwqfkCrurEj7aiFkmd0eJF8WUso2Z6AAAAALkGeakURLN+CHmGW7/MKreRIEd/8QfLllKWohrf2XGUEjDty3V/zICAlat/0cmEAAAAmAZ6JdEV/jY0N0RG/BawsJgKYUujP+rhnqWcqJ30YzX0c3+Yd3EUAAAAkAZ6LakV/j/S/wWIll3CGdQpmmnlHJJJMDXC3Dufc23w6T4+RAAAAUkGajUmoQWyZTAjf++5XDgdzj8QavWtNM5/v7SWzbYKyz+gr1lvvDdabJmin54HFA6FS5USZmz7Qe5i8GrS/OeBhEFUi7sU8j3do+iQgzZ6NN4EAAABqQZqvSeEKUmUwUVLG//vhUdhErrGwMwT2ApJmJaluur8WZXKPf3cxs7c2sP/Xbjfjlt+FJSs7mUuT13MbzM+TrSACCZSMBoDthPFifoGGtkxEnj+e8h73JprN7phM0yaCdgAO7cfoh5J18AAAABgBns5qRX+OkjwIvXb1vb5y58tHlKJ2YgMAAABUQZrTSeEOiZTAjf/7wZu7TxYHcd1Yn912Q4e6YwUip/A050OaFjhX6O2qQXp5VjUL059lIsYYgSQ2jExa1CDIwChKJheC4ssji0HjIrmDdIWl9Q3JAAAAOEGe8UUVPN9/4NuyfQi2HxhNnY/bPczyhQbbExzc0zWVB0vRCS6njJQu39UWTWCDaVRAWBLJEnaAAAAANwGfEHRFf452ZgVPqCEm6Wp074F/bJrDI8pmAHDsFuIJwlvPUbZo3jO+9e0mbvECpBl/WwaROjAAAAAkAZ8SakV/isvpAhCb6whUJeGpnwag9sUF2UycKFAAIxuGur9dAAAAVEGbFEmoQWiZTAjf/AOiKyAj+gnj0a3Gva9CNJ4js1U2x0aCm2qxyr5dY19k1pZuImuv/8peW/h4jD+21kSZLMKNT8K6Qp1NQB1Oz3dayRj8a04cwQAAAFJBmzZJ4QpSZTBREsb/+8PuSK8cB7lWURgB2UuJkbbsVfmYf6uGo0ihSHCGRIUeOkGwszI/RafeeJLVFb72xB56AeE7RHwA6KDK2FMZEiHn9cDBAAAAJAGfVWpFf42Vo2qm00Dddqve1Cwh67h8F7hLVO0VUOGCP37hFAAAAEtBm1dJ4Q6JlMCN//vDfCqSMgQ2VRj3pLESWqFJI9xvPW4RklANZ4xZHF71Z6iQq6DzlYFf0+Q/xmZ/fOpxmMDAoxgM2EezYMdxGYAAAAAyQZt4SeEPJlMCN//74DjgQ7ZH1LvBkl9O69N9KO37jJz7QEaANfGvJaL8Blb2Um8UToEAAACFQZuZSeEPJlMCN//9Y1z/dD7lReDd/jih+Ld0YtRndvMbeeMTxfw1CN28oAhAs3BFnwK+WZTwEg2f//cT8FjRJI69DXHiU0hpAJ1L4ZcWUkQNn9/F3WTm780WFIBn83SpljWZ8Vra05F6qUNyjAMjVTOd4yHsZRD4BpeH6ERAEEj9uWg3dAAAAEhBm7tJ4Q8mUwURPG/74Djgdzj87YkIIqfbdVCHiXmoEJTcLeAuy7kDg1f7oEo7JIcrQiC/U+8t/yN57HX2mDX0MQUw+qGQfYAAAAApAZ/aakV/jnhoov57JnRJzUQ582/A+KqjcuQgc99QQQxsklKC4RUIecEAAAAxQZvcSeEPJlMCN//74DjgiVvqHRYbDnew3k/WoU8O62m6cW1f4z4f2KkM/6uwtCkoygAAAGNBm/5J4Q8mUwURPG/9Y3sbYDUySihk0ZwYHPZc98GT3bkkuNK+kxG9hw6QhCPLCTOPu0cTsYadgUqI051fWxMxsY+qGfVFtfK/8hGLOVkVoaB0SjGYoDGFSY+reuTO1+te1tMAAAAvAZ4dakV/j/S/kR07/7I2ONOQxpj1EA6tGW/N30/w4dgSvirINWao4hRz+KM4mYkAAABMQZoCSeEPJlMCN//8AtC7wRdgH2cIbrCoMs/ZXvDYX8DxXmXaY94n5P78/TKQXUbvgpq5JuPge7/U+LSYd+1eMFPL7ErSRU/SQWbYYAAAADVBniBFETzfgcsI33hBxsPekc3ZDBzYdeklj7D+yHKGhCeLw2I2cmn91BAltpq2zXE35Ex0sQAAABwBnl90RX+JF8eO8NhiMY7e3XCBBxtbicm//Ab6AAAAGQGeQWpFf4cEWNCU5dbW4vKx29yJfhj97H0AAABhQZpDSahBaJlMCN/7x1VfiFwgJBqttUqkagOHWBpqzu3I3Z1ug1iDen9RqMLuWwSJns/FSjmbZC32wpaRGK22xjoTIf8DNdWNE7Xda1dD+nvhdspBUZZ+XrBk1Hqe2E0wKwAAARpBmmdJ4QpSZTAjf8FqjzHg2B+Dqry9XK4oFY4kpye5WzL+gZiytMQXb5+G1o3IxzRZhxW9aZpjLbUsacNdqbjHaYZ9CmpTlK7FK44jbJ1larK+ACOQSr42285xajESkZG6raHieCShaT5/xNHmQa+kW25//2rjWsefjIOVuKFkvGla54JhR+X5yYFqm/bC16PSKR/h99ZwXzxEyHSds+3pr2CMYM14vyO0YKx2HjFF2rVw+hoZHoyQ0JcL/ReGkwZ2fNhkLNreyaduEAjHZAAX9t8IFb0Oz30qFk8O7KgS9ZdqStAL+KUDJIhD3HAAp3xmSs+BqTgOrQ5PS+Ace+fz0i3bKIPCU8i8AJwZcWiUwBhyv7knwrO1Y4AAAABMQZ6FRTRM39foEqx/3JF1qqZd8W//5kxNE6DYERz3YbVOw/1jyVC2gk+UmHuYnrw/mUZrr6ik8rqKlUQKXKQUw5GLsaQyVoSX08YJgAAAACoBnqR0RX+Gfrvv7yJuu0tSB+P/+h4yK0TNhid5Xn9+DciKgmECSbWRRssAAAAtAZ6makV/2fIPxtyYTe2F7AUed/9kq/9NVDnFjpKyZwUbJZWypoABH7rTJteAAAAAnkGaqUmoQWiZTBTxv8gMvylDZaeIJIsMYkC0ZGm9WiRczW/gYZr7/QtVjFGHEuTg4I7VYpSYfgz+C64TnuAQXBvRwRlD7R7BrUKTMlAKgwmktl2YT4IHIivf+PBMSAKaN8pB9Tfiw85AUHSJGSwlVnsXlyHQ+LCtY7tNz+KOQQRHBQtvFemUE7/owFlAZAQX4ZhH7H8hq2bMUPNDHTHxAAAAJQGeyGpFf6zmbbzG6yAo5agB815PT4NIYmF3dRluJ6VKQoZFLbUAAACKQZrLSeEKUmUwUsb/x6mJAIU6OOaejU6jOCEaJq93ye5SDx3rwHIMSwuPbeEPkFOdU1+52yWbGid0yNY2LO00ed66IioKOPe6doVimRketNB1H1k9nKGLy24SyswHs7o8zUFs33/dimS2jZD9vA6qWcVwUdD9loJxKlVTdWWCY05iyBBnRI+HStFBAAAANwGe6mpFf9DFBS+peC/yI0i5BOXZ0Kb0Jtornv1yHk4b9Hwis05VfRcTVsB0+56ooWThOraMfVkAAABWQZrtSeEOiZTBRMb/++FR2EKSC17OilzphaOnJfb+b2v5DMpyB0LE4N5XbFR41vnnYVmX71a1TgMbvI4lkgGITPRwjYOjeSXOpCDC8xaYEVB5J45bl6EAAAAXAZ8MakV/jpI8CL128HDcV88rEWHEY1EAAABRQZsRSeEPJlMCN//7wZu36cWD2WN+3hTaxcsivWm9AgbVBHu/qp/N6ueiOGMMY/MGj/ljaJqYSQxBu7OQessdVRPOqKh231KwV4eUePFzdTULAAAANUGfL0URPN9/4LLaSW8U2BK02dj928jKfghGK1PeCa/SOciAikbQHsE/9VxvCM+EdP4P3D1QAAAAKAGfTnRFf4Z5voDwlwQuL1OkS/asB8/ACxEz3gJmWfQAj0g1qyhfBTkAAAAfAZ9QakV/isvpBhS2mK1fAdwbtVb5YgmyDpirrorcHAAAAEdBm1NJqEFomUwU8b/7w/gmRt4SJHtq/4CnNuN3iEw3xnwnXZMEbxFj861j5ny0cBWKgDehupwGrBYXAeK776eEXeWDNQa8lAAAACYBn3JqRX+NmH8JJKZfXQ2CWI5tPFL8LiOYvVF01NUI0yeIVFZaRQAAADBBm3VJ4QpSZTBSxv/74DjghR/zuyzH8Txd5XruzDOHv9RKkKOyGWsN/oiFcRlf2fEAAAAsAZ+UakV/jZiVZ6ef8q5U0fD2jgZreGAFGFOTGZSMcvYdOVWw2qIqwnqo5s0AAABPQZuXSeEOiZTBRMb/++FR2CH1+D8+1PRw2eC7cPzHLlSr4N5kdbXkTkaLMOtNZYDvN0G+BcnrfD919fNbcNCPlQajdZJesGGRw6UvTyq3mAAAABsBn7ZqRX+O5cRG3XQzJOw4cZkp7na6PmOyn1AAAABTQZu5SeEPJlMFPG/74DjgO8yArnokfcqY+3z7vgO3k81KQrKISGhM7SavcwZDj+Lf/9hb+KMuOUdKKwHApugri91Y0dc7DkP6jYD4KNCu/ZrJqskAAAAnAZ/YakV/hww5FAKjLlfq6Ljf/+1coHIsbogAeQqZ3SIn7qkD8EJiAAAAL0Gb2knhDyZTAjf/++AcgTWtokUyBqD6lhgvnfnJDd8Cx3z95yizbJFdyDlHg9jlAAAAT0Gb+0nhDyZTAjf/+8PuXcpxiwPb1g8GbiEE9IET2gffzB52f8EdNr3u9CTCJQDIJOrkhycCCgNaBG2HkcB5oPyRivqaDJ0+sBXnYINEToEAAABZQZoeSeEPJlMCN//7w+5IrxwHpn4mcnp3qC8i1PB17ucNb58sAdjpq0E8ox6I6nyB4tsTME3vaKjnv2HOUOKh1xJET0cCW0OPsSokk6kSsHBKYZjYC7L8lyAAAAA5QZ48RRE834ISoy161qatNvHcx2P0v+2schVH4rQF94qpR/q56ZD/z6y5NPUUhWN3Zgno8YGf8JZBAAAALAGeXWpFf4/0v5DikvANg1WUUB+NcTU/sSU3fSaYjuWP6ked8ltU06WZ15X9AAAAWEGaX0moQWiZTAjf/AKPrvBH9CwqRiXu1U258fBKSfqjqjBQd2g4Hb33Y/0j/xa9Lykql+WIXGWh5MAUZG4/duV0oRy89osMMauqyiZdhBQPluQQB/BxcR4AAAB9QZphSeEKUmUwURLG//vBRkdJOweuoGYmpH5xFZVLyGMWYq+kIwnpHpVXYdaSzJAWmGTPK1sFf6Znkp4MDLWKTIwxvdVJ52cC1rehXCiJGg1PAy47lCc5h0zQ7J3gBJjV4y3ubnbO8ylb6w+rjLP8+xSx/E5wYWiF5YEZn0kAAAAeAZ6AakV/isvoz+8v1Hb/P0q6S2yOKt6t2F+aO/ZDAAAAfkGahUnhDomUwI3/+8SDUbxwHf2NVhEpR83q6f+XXT0emIrLG/GjTA0ZQEsuf4ilRudQYS+kcaQP6NH/n47oeDxDgr+9I1JylC2mc1pGpx+CT9kp21tNNfIR9M3h5hKBoKhhH2mmDCHWwTlWZrU8XQRkROFjiGHTXpnI2TQPfwAAAE1BnqNFFTzff+Cy2kloXh3IfGPf7xzw0VsS6ephFl5SkLNj4sIWol/tW/UCeEA0PDXCAF0fB2wHJQCr+HvgtkKvByBuJ7pSQYYcBBA06QAAACsBnsJ0RX+GfrSKAZRt3+eUjEP/tYzGZCzSsy+iSHSWdDdvkdj7BDVckq9AAAAAHwGexGpFf4rL6TwoJvceXyel974RIYNEAeX014ZGez0AAABJQZrHSahBaJlMFPG//Xblp7QgISvXq/43tYQswnlCiPCtggpbgu8Gcd0XnodgB573BnhWBSscJCb+QLXOkHexkQ6chMI3P2elzQAAACoBnuZqRX+NmHWKNnyOWkElh6MU5PvSNQ8zujRaeaaC8dcoGksLYZPxasAAAAA1QZrpSeEKUmUwUsb/++A44EJXr12WY/1R9NcvW6Qg+sKr/ahi7wg47WJVdC+uliZS+rcEC7sAAAAwAZ8IakV/jZiVZ8+203fAhmP6SQ+/2wesW6W1rbx/GmEeycqK/2uzkQSaEM7KzV5AAAABAUGbCknhDomUwI3/wSZXK3peGISKDGuEfaX/ruGdURoMWCqwPVNEpOv+ST4slfLn0vfGnqFijtJwvKuTeo4gMwMlZ12WAx97eksVcC/xZyoYY8gEMuJoVZGXraJXsn2AWjQmljY++Yn0UGbvnrgKZ/T7K9u68fYiopDXks1XdSedLDYdCPWc87cOG2LaWaYKA+J/Rcrt93JbbJphn/RaqafF3b1J+VrUGnuLXN10mq9Ji4vhD2gcCVDgU8QP7fngj8k++0v6zvjDx4MM30Unq0Ro37cLz9Fp4dYmyVWi9EBFqKvmSgFrSgSu7qNhL23/SWg/5DKBGi7SSMBZOnX9WLFZAAAAs0GbK0nhDyZTAjf/8pF6MHN8xvhomJb9VTbd1oufHXVFvOUqiH8wtZul84FilJh+DP4Lre0QjSMquDwJB1Cd2xjlRf6f/EfPHC1oSOv7JiC13e/9H5Er1hNYw4FigKpqQ6OH9tzFrQITEYT/f3gAuicOt4g9cvzkdFsMYBTJT4tnA5QEdXJC8nEAXWHq3Dr4PL5tNfHtaPyUqNoDKx0fsKN4D/mNRoRUv/gxlV5JxljuF+HlAAAAnkGbT0nhDyZTAjf/0Y/fwfBPYIt2Ih+oVDWysEi1BfcGsNc42DZ+iX7NLlxL5cxymUJMWBhkKF68OtNiy9cj9+WIlc5Mhajqm9tEk7Q5HF5llNkQ03EIGx1nktYjyHoOPCRnfHGTijsZ7HClYxAmgkK+zsXNRz61pxBrMmmixPZw347116XCKl706m6iOHYY0uNsJzXzWYUyQe9L9FINAAAAMUGfbUURPN+sXvx5/CiDiEacNaS9U6HfVvnuP/a5UQJlGkFDYR2J/hU3JICcol6Lc4EAAAAnAZ+MdEV/jylvx+CPXBU+8gKTE/8VIElasIGY33llIOtASe54iQcmAAAAHQGfjmpFf9C2JxDQM2FrtfcernQzbgQo6cOgAiXIAAAAPEGbkUmoQWiZTBTxv/vFEDSrIEiP+dm2fRrEThE5lxbkgROmjfPtCbfhnR3G4ID1iPeJh7HY3Y16Odh6bQAAACMBn7BqRX+NmH8JJKZfXHhkLwpdR6P4XTfNRE6MatJ7PVwVpgAAADJBm7NJ4QpSZTBSxv/74DjghR/zvAWMRwWuIY0ILoghoK+2U2wGoNOA06nWHBwAcUpagAAAACsBn9JqRX+NmJVnp7PpUa8FkOcbCyJNunfz/M06nD3e8NZpyq2EIfrhbd7BAAAAT0Gb1UnhDomUwUTG//vB61Ydg3N6LfuunSF3AkvNLoQBhoTQ7aoVG6tsYzS+JiPd3mYuiPkzCyWnK401zAoTSSFfgBeqE3OpyvPlsMfUq1EAAAAVAZ/0akV/jX4K9aDUHTkIn8NGrTJxAAAAOEGb+EnhDyZTAjf/++A44DvMh5EHnmI+0qZng7g7n1G6u5/Aqyb8TemoaU8nwXBzvg6L4ceEadnwAAAALUGeFkURPN+D+24e0TinWPeueGoG4sapy+ObB3xDR00TdCay97Igcx39n3zDVgAAAB0BnjdqRX+HBFlCUE3nRNtGVVInyej3t3vz7uZ2wQAAAEtBmjpJqEFomUwU8b/8IvJDICKyxCcdzT/mboi+flPNWjFbInDCJbwHXv4CBeLXeVVCSD4rO9Def9jVQKiZ3a5b5Bie9xheg2SLH+QAAAAvAZ5ZakV/jY0N0MOVPt8h6WcoZV1S20MbM4DsMfLQUbNE8rolkKvz6B5iwJ8mvWAAAABXQZpcSeEKUmUwUsb//CJ/zICOuOx4SMR3pARtPhD3XWj3rBkO6OrvomzLoOGgKYE4i0wu3UHRLX77PtaCBTI4Tfpc3gtTPNcmsPDp9zCw//wDxv5JJCJZAAAAJwGee2pFf4x17dolDZL3wxmEFIgT8nhorh3Yq+I4oAacvjWdHciZNQAAADVBmn1J4Q6JlMCN//vuVw4Hc5yyvTItaaZzkkkNHLJkI0HEoJdIeD3dJbzkXmvtq7jUQZd45QAAADlBmp5J4Q8mUwI3//vgOOCIxgOqHGQ4dPM9u73R4ALKr9BQr/nU61eDXxry868wQq2NQQB2Cw7y8YEAAABaQZq/SeEPJlMCN//74VHYNYZ3DjtyB3KfJ/ehNVbvQ16Epn16L7ahC4CDOiOWjEm08H+S+lGobDN1/4ONf5WIIOky9RhRHLV6H2eUZ80HGf0olbdDoIPuaghAAAAATUGawUnhDyZTBRE8b/vgOOB3Ocvj1S4/IKY+GUDepFZA7JQ6D5zJgHdmU0gV4AyG3PnUUlwhcyp1sj/2sUr8E80bHPfsNfQw3EPqhkH3AAAALAGe4GpFf4cEPpZ+ayGHCrzmIFkFJJ5iOG3d7ko318enKcteEWyK6vDU0azwAAAAM0Ga4knhDyZTAjf/++A44Ilb6dcZ4GoPqU601qIovFnKC7lkIeBkBzqp//sZLPSn67pyKQAAAExBmwRJ4Q8mUwURPG/8A6IrIENlQqhLiwoaJhrnWZk18jQ7ylRVYz4q3Y43ERkjNUVHICbJU3W2fPPQUmUyKqzovlWUqaoQrc8y075BAAAAKgGfI2pFf42NDdDcJbtDdK7SWJH5HwS2s2t/lM7DAAfmUTKKRr+T4pF7/AAAAEZBmydJ4Q8mUwI3//wC0LvCDPTwaCjaXZ2btf9j1UTqkuJrlFvJUAop1+VQkGMNp2GtkRPC5V/i7vZhSjenabTSd4ZI6Z77AAAAHkGfRUURPN99TaObewep0G/CenyT8ihy+yfmI16uhQAAAC8Bn2ZqRX+NmJ1nz4vAdigsChyYtloC5NV8EcFNRJflhn+2noyyo44OPhMN8LgKTAAAAIpBm2lJqEFomUwU8b/7wetRFII6Zm6QidC0oo+c0Ns7riq1h4ZBWxZZD6JzeHG5pZuCUPFJCaRVGlMRP6GhKFXz9x3DH2KAgdWA1aMZo1pkrtviY17lc9l1KlNpbFrOjLf/sY3BYOhEQqMpFO89k6PKn8sRKcq75K2ccdT4r+p6kGpG4mZrOF/98uEAAAAZAZ+IakV/jX4K9aBpZl6QE2eOe/h3YVFcgAAAAEtBm4xJ4QpSZTAjf/vgOOB3OPztiP2ULSp7XrKrWw3l4Bc6H2W69Q6oSoF8O1hPrRXjY7we9rAJKPYETi17uxoi3v25T98McjvFdpkAAAA0QZ+qRTRM331NqbcLGtWbMNY3WScBZQDj4EVhJKd+eBPd5akdHADpGT8hVb2bsA3vvcUMnQAAADMBn8tqRX+HBFjWKW0eRGEWy1dqsM/Zh2+r0j8TMNGs/fPMii2zXF0N6KhYyAEYzOLy8UEAAADpQZvQSahBaJlMCN/HqbEHRtdjNTKn+1Ks8Fd7U1CyW72yxoAL49N0mQ/rxd6hIp3QUmPFE+CBT6W6w2fg+GvkDoA5rxlfTH5iWO7O7X1aIQrUycIpZ26bWxdE69CfvW5Ump7bO49JM3Ojp1ba+sFXZJgF2H/6c/uab0RGBZJFVPWM6/2iD4z0L1ee78rMROduc0TjmUMv4F5e8iqzbVmWZxa47HR2pX34hnX7bqBD1Y05md2DIEIMx7XWDS2zFGguo8fbZRpICoATJ0iBpYXY65NY+onyXag/KvGTn9dqOkIZN5bgNwodskEAAAAwQZ/uRREs39BT+V5BdRxLLjvRBZ/8QK0LAnU4ryUT3nxiHiAhHRZibTVIPm+0xrfhAAAAbAGeDXRFf9BX5Uw8x8UvZrjTOa6N0KNc735HzeH8dCL7LysfJAtHc6CJl64MM87h+XsJUfWPW3okcUNQiLbRiB7rS8Stpkk7rAmn2NBtaNQkEAWDZlBKfeymcqvzqnPiuE+on+yiIr3BB71tTAAAACYBng9qRX+NxKann1KePz6kv+QI+9/UjpccvAs8AqKrEhiFftqoFwAAADRBmhFJqEFsmUwI38oIRG/MCA7nH4jDF8og0z98ssIrvm0ivszdpDx+Nnn5Qb+Dz0hEkbmcAAAAj0GaM0nhClJlMFFSxv/HrNHv/cjBycDl2vih6keQw56fCiMs0YdNmpp7imPBF1y/lLKBnxMxn3M9E+3yBzgA4qoPmgxAa8dNdml0VORbm0KUx6CwEuL013ZY0JgiO+ELoSSDqSlaWOxREn4lWygn7nk/erfNPMDpvIC2jYz5OlVHNcLkNnKjrSDpnPb4eSDAAAAAGQGeUmpFf9DEtYTuNuuc8MFIfiTupN1BdFcAAABQQZpXSeEOiZTAjf/5KwLantfVy3VySM0SMHvsr0YmBrv0UL6DlEtaxQQHzUXE30YsdNChYm2O6V+hEk4F96/91dLwu/ndfGK7GATkcf47DIEAAAAxQZ51RRU833/gstpJbxTdFgmzsfvEjjhs3dxWp4ZZ4kPG8NZeKU7GYzN2OvKw1tlnqQAAACUBnpR0RX+NmIM6D8IxZFFGEALyP/EQHxDjOvu6mfZR+X/XdtvYAAAAHgGelmpFf4rL6VHGE31hHDEGQjtVce1yEtA1rtWqwAAAAE5BmplJqEFomUwU8b/77lcOCFH/OzbPo1iJwiLqg8angbnwoKqYau4/Tr0/Su9hg7+dXmJB7llgNXjWmAvP+6nn9ZyA2iVrXntcHxIEctUAAAAkAZ64akV/jZh1ijZ8jl2T9B54JFoOBhttQMJEZVln9G5lKq8BAAAFQmWIhAAn/74fcEJGW81yyeQ3yjIJuOQipq5DurWEjnW3UorRPL+Gmp7W/eH/ZijezhQ2AIPIQl4aHdQM9A6M5/MKi9xGlN69wV4R5VA4TQl0Sllg8188tv6vFr5S+P9cmRKtZwlxsQrO/PUH7ZGWYYybHVXFlEMY6Dl5808LYw/EiDr5JaoSWsHu4ebowTZo7v7qaSld+s+T31N91+CVDr6kktqJbNFOPAo3BvxQkltR4XwG+/rxvXbxkWnnZ4k7z5WpzDWYcBFPvUfTgrd7n6SLHLTp7bJf8LFMRC7GnoqmMyaPAzg75u8JidsVPIR29wsvpk/Mt9R6nnF5IJistydnX1nYc673c2swNm0ubNLgupd+k5ttRQ152rHLvBca63qjaEYz5bO6uRdZGmNbrdIR9DOFJYzEJ3T0VRVhLzRbgQhmzINEkMybfXp29t1bR5qya1mRWgLdBC5l3drBLQCcVoq19LY64FqmtYeURGFQ+dxCLISD69Irv9a0uyOxS7lVS3qeo5/gPB9YhIYgKQn6bpUQv8jq9Ep36A1DYX5VFcN9XqGkifkeY7ZbzJGH1tSe7aDoRQIiDO6HnmttmwDbytG0SzChvNEtecWmFp6+Ng7jqZmJ+LpXMqaVGcBbl1ehkdm5Vr84GAQKEq97RhEnaEuoPQsiUhi7y5CzHQA/q9/VpbR1zqAOP/78KNEfdl56fss7Y4UfTIZBj/Ef7ScMR78inLAl3j2HLP1BrnE2c1Zfp3fmi51t7IntWomhhRLlKfnfJrB056GmNbnqvtmZCvlymbrND2EqUvjJ1EJ+n02dodaM+1EsP8F2MT2Y4tjt1aBsu9gTs10CfVny7PzZrIe7ZNf33r8LTwpXSowlA4nQt9opFQ/ZJxaz8Upq6XgXe6Uqzu7x6Z4G+LEevAbdMu5y5ewVRMuV7HqXwlZX3FmRKJTwfg2u/onfLxKJGGPlejg4w6xVcIvQWpCCY94rqqRweESq52TT21mJ3ZWZPYd/bW9E4HoKBWndnm57+UuTSHIPQ35BT9W1Xnl9r9CViMvIQ8AUlOp7+ZZmJtCkSP14cMcCJhNiLGBPwta/Oyraa2eYe4xSkWML2OCXs+aPKBwLcn9R4ao5D1S55sxD7n6+Mg+z9jwufgfWX3u9ii7EiGIFM7wV1HcRH3cSIQZVgHx3bNHeN9HrMdz5R4HKc+XWQ6nfPa5qVL+jrteHlNOWPkjRWBZgYim28+DOXpSL63VQKM8wCalx/7UFzpmPLYi2k25Tn4L4RY2kTpMyq852ckgqQ/JuHNjmCM0OZxxusMdxMp9ba0q6z4Fov+fjMAzn8vKAAJKzFCfdFJg2MeZhfwlKaitFkiyEY8mnat72LBWhX8y2pFqa9wBfrk6vSpsSIPf1ItXOV5dsbkmQaOOvVHpr39w9kGJ06MPgDiO7B/qm2Bs3AlNEuL+WG8zu/vIRJdDz+RTRIk7vLQr0Rq8jEQWZ/euf8FD6BUNHSXZGM949sWmTK0eoMcmoTzlyGGCQgdVdRYqeVPDF7SHBzjIXvdTeaPeF8rdbHCdMCUN23hsQBtwTi/WxUcz0MCULeSfoCuRD9EomV9UBsyxfCUqSYYFieuAbg/IP3y2xj+T/9E9kqvzPRPQa2y1WxDCMsnMIy37FnZ/7LXXOsxfbVeew3U1YASM9XkT/y50YD3lR6WGJ8MYfYM98D8exb8s0epkXWDnGeNbeKqayRpRsYTvsgzojgHxpi5+wGkEyHCLHk/j2PjOYfUfWmKhOwj053UqWFuvky53Z+AuX9yWhjuzKAAAAOkGaIWxG//vEnVTkBINY1xBnYqV2GYveJxG6pO1ytvZdPbEXt5LdA7TGVX0iYnM5z2Z+J5AJNCh79n0AAACQQZpDPCGTKYRvygg25A/mod1FrlSV9Bg4fSRIk5ApsXKnR4Lw1hYENtNp2IdrhL36xanS2cwpV3ghaBQ0ENK/+61iJnFzrglvYgyAeUPfvh4+H91OONFJ0BQNQP7S+T1dX3IH4RbbT1dKFwTvZ3ZM7M9HZq2WxWBFVnr9ZP/zxD+foUgnXU6TFFBNN/llQnxAAAAAGQGeYmpFf5XT+htGpxm+ttzhrI2RGOltiWEAAABzQZplSeEPJlMFPG9uJuUZbUPQJIkFcwKAtahG4ohsmW9O7pJ3QXEf2moUd4qKRCCL0ZKGjcHbIVUkGfh0CDhI2ibslmbDuzOMAhXG/zv/6v/5h1pF6Fnk4OU0BdEQ7aG8XM909Bk3XHm49yHVcqbBFgXZxQAAACoBnoRqRX+RRVaIVD1YrnHAZf+m1ih7YZmVdEVTUohPyoJ0YoX7wwFZW8AAAAAvQZqGSeEPJlMCN//74ByBNa2iRTIKCh6OJLaoh8ps3rY7B+aDjQEslblmkDmrpFMAAABYQZqnSeEPJlMCN//7/0a5QmmqHJOeKUzB2eNo5H9glW5oLygWzEVYpridRvXn+2AASXaouZZC4r0hn95olSpqHwrE6msLwa+V4Ttq+yvbjDu1Y6/bkwQegAAAAGFBmspJ4Q8mUwI3/8oIQPYmOP/wRrPxM5PH+fJ0mFWAYBFh1Pe6xJT8//aEn0KlIlhE0/6putAFmsffwcLGQqaabW0OMe6iwCBaM3Kq2If4TnEQ/lSd0QI6JJrQELGDib/jAAAAO0Ge6EURPN+Ecn5eBysgKBkhDY8qsL/25Qam4wDRxrYU5Di/5y+wAXkIetqyGrdnqZuLCcwFByy7VklxAAAAKAGfCWpFf42Vo24OWiO+wC6Bf1+vC57IXLwDKAPdiUIokWez79lHX4AAAABIQZsLSahBaJlMCN/7w6dR5vCGznNtXd5bO5Zy4LxzmfFyTxf8VH6vp60ELz2qk/FRPVhaQsbXkKgrOqn/+1roIltiQuL600R9AAAAakGbLUnhClJlMFESxv/74VOAECNfo3TrZkzqu0KkrOZ9CVw2gjMRNMj1E9vxW5V1Ecvm/9qH28Rc3ffR5r+zqVMsLdHEwsswCE/8g29m7hFIyoSbEObNEnebhqoXvPeZCDCweyiSrLwsRskAAAAXAZ9MakV/jpI8CNGNvrae4VovZPXKoXMAAABnQZtRSeEOiZTAjf/KCEDu+rkuXwR0ltpBueVY4aihZ6XD3gGzDCyVqxs5vXh7SVn70nLyVRSeCD7n1+k76tXWOU8Q8cvlYby2Hnvldl8uqwEjayKEwX50N3/0maF6ny5kGq0f7Ao02QAAADxBn29FFTzff+Dbsn0Iut+RPjHv9yMXPXi4QWBtUuz84rCf2ne6MMkRmFjCTOGYGkE76dyp3SNBzeilrswAAAApAZ+OdEV/jZiDOWM1Uy9Vu0nWL0V/4HEe7fSSKWekx5hQYtiC/xgqvYEAAAAjAZ+QakV/isvo1lhN9Vf9YXwem/sU/GB4tK3zn/fFUPpEfgUAAABJQZuTSahBaJlMFPG//APDMcCEr16wAPFV1G7zGhko0fBO1LBEbSo51CNZnpYS3MHF50L4MAKNELOJvnOk8fJx9M+aEnAehC4L4QAAADcBn7JqRX+HDf42NiA1e6IoNY9dd0+pse3XDs23QkJmYymj6mByBQBlbsNBH/n+C/Zf0yypZhu9AAAANUGbtUnhClJlMFLG//vgOOBCV69dm+FVLfTWcUGQr+lSqCflwO8IOO1iVXQvrpYmUcqC/Y4LAAAALQGf1GpFf48rV2va6Z6CX7HZbnuYgcin2b1jZsuvhj8zrWfr2ZgMZiNJsrNVEAAAARJBm9dJ4Q6JlMFExv/BJu5KzDErXKYS1RifWXWjNyUkZ7LuAhIUnAIaOsVEMMQXit7waW63wzdcUIeRyXc0ns6O4xhmDwZ3ukGLdi9EU7w+8z26SXTja3sGWB+uU2PQaFr9su4rpTbgme55GdSV5Hium1b/ZY7wn0ziN7L7ptbE80Ax28UKDugysLJcSwfJeTU+FsCjXuJC35Kp+sq+nt2e0C5pikCgY4m4RGYJRGW+kGDU+yhW9YEtPxxAt90wlj4Nc2+02VtndN2Z1aSOvivKWQperyfJK+RiYH2Ua4M1Mbvt+F/gIq/S9qIVS9LvpsrQ+7+gxnXyJvs7t2yv/J7ljPiE5MYPyl8h+jNIjrv8R+MhAAAAUwGf9mpFf9Ei2Luv/RNJcaZxTnDa10UNbuQPAlqODVB2VxsIpmJnfjPkMJPHAq7sCkJAYLQ9TOcWdYY/w4T9TToTqOJl5XsxpaKH0nZtSdWfNN6EAAAAUEGb+0nhDyZTAjf/+8NYd2niwO+rMxV+zWAA+1JtHTaumj+P2U+PhhfgTzByUQbNihyr6FdaXSQST2LLyoRUr2SQPrdTZkfv31QscDSS0oRnAAAAM0GeGUURPN+E8DKIQ4/14uOVqEAMADp0zauP0LtNNslqo9KyDu+ewIdMlXYKjxCCIZ2RgQAAACYBnjh0RX+NmIM6D/Z46G0zEmw183/eHh4odUNrt8tMZebEjulWUwAAABwBnjpqRX+Ky+k78Xe04HkuIKPhhbbazcKxG4nlAAAAi0GaPUmoQWiZTBTxv8epXycoOFMLM/d1FqVzTzgCDwFATJBP1U6ge28/eu//0BsTgIFsoE4M0gMczwnHzJtkURt64dKffbzBHukviZ6wJ1DD10/gHeIjw54CCzmpIwySvy0eKrn1sxtgjxrAjk40cMg2w3WH2SwB2gwRBdRsg0MrzvNgn9GcNQO1ZTQAAAAnAZ5cakV/0J4+WxsQPcekdWueGIREStNdZlzFmXh0KEniThU6N9wlAAAASUGaQUnhClJlMCN/++FR2ESuhE+RatyVDTTzS0MEydH3+Vpq2cFMFPi5QKgi9czxuhaGemqsD7/JcIUhVdt886og+ljZbrvCIWEAAAAiQZ5/RTRM34OpqLzLMBpZQ293KhCxlhbjxhPOxHd4q2TqwAAAACsBnp50RX+NmIKsD88pu+EZBTc434H+uuuvMbHJAB0tuUvJ1rP2bn0fEH47AAAAFAGegGpFf41+CvWgaWHE5ho1aZOBAAAAOkGahEmoQWiZTAjf++A44DvMfnQGvr5aabtMPFRgZrO0PAPTbXug3OYp1ediQc1+3GfuUR2A+YYULrgAAAAtQZ6iRREs331NqcrGn20AwIJmTxeb5XHCMknUBifcKi/SGXaUJ0L8hQfqmM7vAAAAHAGew2pFf4cEWNYpbR5EYMikZF8nmrBIkdnpZdsAAABOQZrGSahBbJlMFExv/AOiKyAj+gnj0a3Gp6zI2CjNAAJIWgyCThvil0kvhxqkZ6Ht1BxxqXfrjYLInpyi9Q1SHRusH5zdhyC+Nu7rF+RwAAAALQGe5WpFf42NDdDDlJ55Cx+Tl+h4NevQbGGML2wsKXdErYWj9A80AousAs0tNwAAAFhBmuhJ4QpSZTBSxv/7xrXt8jlCNDUuxflUiLTs/g36A9cpqCK92W6XZsUT9pVvqv4iI3XXl/siKAd7P64Kbe2lHxIRZwTXchYXnFFv6PVahPdtBRwdIEdgAAAAJwGfB2pFf4x17dokp5DlQDbrpLgfa2hCiFMmKnp8wA05fGs6beVH/wAAADVBmwlJ4Q6JlMCN//wDwzHAd5j8QdbEjP/MD54/nxaJsM3etRkV7PtoitdWOIiK4oua7/OJmQAAADRBmypJ4Q8mUwI3//vgOOBDtkfUu7/INxPIL+S5L2s8+0BGgDXxrzOfaBdvcLkOu9kv7y8YAAAAhUGbTUnhDyZTAjf//CIBv4QEgt0jB6sYApnNKebF3UduVNlxp8UDV72R8VAklM98n5lOWEw+T8M4WRe//ioMQECXsBcH5k+vJghjfT5jqlmoeL/l20XeVcwcUIuafvxnJkzhBlcn/d9IuK1j3bCY8QS0oTVT1N2fu8R5Tv1RgAbc5tBoWuAAAAAhQZ9rRRE834H4ctY9gURT1dXKSAIxLkivCpURU347a3c3AAAALwGfjGpFf42YllmapQL/etyKspbtOBGV8f4EVMMX0hi4K6l6e+YACOn1K36gQd74AAAAMkGbjkmoQWiZTAjf++A44EPraJILnXuLxMWdODvz9zlGHYLFuS/WCzov37vtypupywZRAAAASkGbsEnhClJlMFESxv/77lcOA9jsDFR8Xdn5jdZFKfM583oBslJ49SZxnRGp+moI9oSjotvQRWZD8gOf558xXUMpWRXJ/cWn7ZTAAAAALwGfz2pFf42YnyFLdokqnTK0mFdGbVl5+BNzXz+jPJsmLNM19mnectkM0k+LwyFJAAAAPkGb00nhDomUwI3//ALQu8EXYDhpIvttv8TbZDOP8OLBMAO70WexTFz5+cnteMdktY7P65A4tZlIDdklK0GBAAAAH0Gf8UUVPN99TaObewetr3iFfIM29vQ4/CELIo5doCEAAAAtAZ4SakV/hw3+jEmnmFEtcwfltCMFzrX9aTK9FUCy1cKpO11Befk4O1gAKZFBAAAAe0GaFUmoQWiZTBTxv/vBGT5YQIcUNUZpARyxqc+rS5mmS+x2rMYuZzxaaNzjVUhTcMNlid1G47Ff7Gber1siDAOxLdQ+mOx+lSM//HIlVdSI36FhpMg3eSscveUe/EzN0NXsxq+B/YFxJBXKltyUDxpD4j5KmVec/I5HTgAAABoBnjRqRX+O5cRFOX/rc19dAGfoPBoZZ9JRwAAAATFBmjlJ4QpSZTAjf9RUJUwmPx6d1qkD5IiUog3fdDYG+lhD6PhB6357xt0eAhnE79OehpzS9hNg+0rld2gpMfPN78NladMHIMJnu+IgwcdY5qjgIx0rHEFP382Z+0LcY8oZUMOK+1SYe1NyN3/A/j9w4eoQ3PNSo4Ux/LOL3AlYQIiar3GTf0B95Y8t+So3rBxsftentdD21ZJ7hAbvgAAT3Nv7jjPdzJ/O1zUUPXbzz+p7nvdFPTkoO1Dqocj0bjwXKlp/xDJw9QeJ9FCKJkVoMOYhplLX41md7oQ/Y+qGl5+5p8L8BxRb1OTUS0Pldi3oqvL9d/Zrz+oryE/RRst8Em/0e7bxnWHG6Mx81D5GpO0jZK4XqbwX+2UQU4boZy2cX+Sjuvn0MsMkR236Z2K1zQAAAEVBnldFNEzf1+oL/lUB43iexxWj6oMO+zCK0ceAc+a/F/PKiFsvR2BDrZ2PrBeRSYr2NA9GNN54oqHc2LpSV/DC5yGHm8AAAAAnAZ52dEV/jZiDOV4WjRVuqfuRLr/L1bWuM4bOVk+yHw8xIsSbICVWAAAAKQGeeGpFf95p6Ti9RhN9YQqEMUYPvg2cqLZ+fsaBkkhn7KTT2jvNMkWBAAAAgEGae0moQWiZTBTxv/KSaxhdVGtNZuOiRf+JqEcNFkbAv/3toQnjkNAaWg4K8+iRR1zNbt1yIR3tJg8Hh+JLp9Fo/JoyYU3FI5nF35wWUzt3yx1MISi809oOoCzZrj//fWbdhPS2477QVN0UR6uqQ2+uEBXxub0LmMRP6aOCVtOBAAAAJwGemmpFf6zfg2l3Ygdwg0DWPIpQmdHx99XujRSHBNtWXm69NnlO/wAAAEJBmp1J4QpSZTBSxv/74DjghR/zgYSVgQLW28saHEGZfNPuk6gQX9d7Fl5UDqoN11nbQFj7zXG9wSS5vhSv3gAKUUAAAAAwAZ68akV/jZiVZ8+10ObdgxOx3183mwEhK7HkZqcvSQ1ycqtgp8jF+U3zQBWvpsMwAAAAU0Gav0nhDomUwUTG//vB61Ydg9A/wlrkMlWCPDd7QTOqj1/1NiCSa8ruC8UBK8w3Ph2mInOfYuW+cn88B5LL/toEaKTUnUAV8fIMLomlaMvI1rt7AAAAFgGe3mpFf46SPAi9fOZie0tFVOsqcMAAAACWQZrCSeEPJlMCN//HqYcZuGVy+Xdov07HU6KX5bS+1WmnLQvbAvY9UlOzUvt+HiM8nLp39Z11G9J72MQOlYTjkOf+SeXNLe5vidAHlSjT/Ore0st82H8qHv9wvch4z033oRI1qboMoq9jVzWQ3z/b49jZCWWEEv8uwxf9swldbmArV1EwuuF91pmeCP9WmkCZPwED0CvAAAAALUGe4EURPN/QWwldz0TioUzxJwTL1DjVPQjAq9Y54uZHmIdkd8NkQOEM7KiMsQAAAB4BnwFqRX+HBFltjCbzyK+FRKnf/mLOjOcjzR76r8AAAABFQZsGSahBaJlMCN/9YpHGMYUb5j/NfcgCQnnnUME/+uYEFab7R5PJRwSPzdjTXR8fgJDcETNoWNTQyIj4LAz2Alc7KxLVAAAALkGfJEURLN+CHmGW8iX1nC8et2f/B+8KsVycqqolFcdZbi0GJHLqpjMjlxYW8t0AAAAsAZ9DdEV/jZiI7Ev5h6E6AXxVvyU+Xec7Xzf+Ujz4nooqJFL612j/wKfuy7gAAAAiAZ9FakV/jZXGc7dkFSNTQS86kNC5UhVk5V114XqW+n6aNwAAAC1Bm0dJqEFsmUwI3/vuVw4DvMd9xLBIuaZ/zxsUAT+oe+xkeDynj6QmOuSirEAAAABtQZtpSeEKUmUwUVLG//vhUdgbBTvAngor8pfCekIPa/E8Y+CM1csCM1vgERqhAjVxRBDaVo8NX0IcDkRQM7Fc+VrjXHrEEHSAqDVNvfJe+IHgsFDNZNns8smESREry41It9enn5zKZn+/bjH+QQAAABgBn4hqRX+Nfgr1oNIjL185KPI+d0MY3WEAAABQQZuLSeEOiZTBRMb/++A44DvMfbdU1ETVNwuYBfSvlXeOFbA6GbqsdQXHZrecjnlZBPP40eDLe0BA3X8hhdWV4c0xWlDkOf5flpvIBUQRHYAAAAAoAZ+qakV/jnhriwfjEBC0Cyoph0KvuuH/nOiAglQQP/F8Pzo5XgfuSAAAAEBBm6xJ4Q8mUwI3//wp75kBPbfTrjPAkRSSgH3g76N+IPxHc4xKee632cn0Is7nuf/sPmuSHgjxKqRUscLPVxNhAAAAWkGbzUnhDyZTAjf/+8Gbu08WB32S1i5TV1gbt9xeW+dzaP8gfVQwdBZ2FJC6dNZtf8f0Hg+4rzvoWkTdTJuDVTqcnkDXxrzOGZQBT4SetMErEEP9qYQ3b8ROgAAAAF9Bm/BJ4Q8mUwI3//vD7kivHAemh1CS8p9HXZNoOrlrUrukxAJgTBabe3Uq+xKUtXAoE33EFB1Jjy8zf4fiuWPtUn1WH8f/CY8QlZPFmMmcQSG4fjuXN/B+qNi/1vyFEQAAADVBng5FETzfhPBPL0/8xgE055y0cUfkH7gbD0hvkO9CUgUIYF9ILO4onW0KnZIlS4Q5+PTxIAAAACgBni9qRX+NlaNuljs9U7M/SW9U7+gZciTVHqQBZINn/LlwAI3T67KhAAAAQ0GaMUmoQWiZTAjf+8GQRIyA+EC0i53Uv0awLgBrmCYaTtddMfGzDz0tG737dau/+uhw5YMVYL7inRfSxfcFiKBNgykAAABfQZpTSeEKUmUwURLG//vhUdg1hne5jlt8Hnsw/yzG9BlYA+2BCvdNQCaqq/vU4NJus/26Yiqay8cHVINjOHjrvNVNNQMyKgANVegEHkn0Atv5BheYp5J6LroUHacyJH0AAAAaAZ5yakV/isvpAo+X7EhbLzLMVxMoH0gf/oEAAABsQZp3SeEOiZTAjf/7wZu7TxYO5AYsHfwK5RdvkSVEEn21E6ZLxQXmV2KUm/dt5YseWbLYmF6cycseQ/zRHyWaDXSPfsWYc9/L5FXY8bkH+03r0tQyCwxscnMoNSAc8JSQ1LuDhPTTMWEyb+wwAAAAQEGelUUVPN+B+HGW0ktTlKD5U7sftibyzFU0itTwzBOsr8jinvAm3X+xweVleORM4VRCN4J1niSdrhdyNeGimYAAAAAoAZ60dEV/hn60g6kYGvRqVwcf/9DU0cnE/S71LZRHgHSKCWLRPYzu+wAAAB8BnrZqRX+Ky+kGFLaTu3V1h5vNMrHepiZHt++d3TYwAAAAUkGauUmoQWiZTBTxv/wIE8MgSI/5zkdt2xF39AwC2NCzH/ix26Qf9Z2is2kaMgsh4st50ymHALuUDhcWz8HLl5Mv7cFHUPup58bPZYY2bD/A0L4AAAAqAZ7YakV/jZh1ijZ8jlpBJYejDv1xMTNJL8wwhvpmAwdAZHfqRUgGccktAAAAwEGa3UnhClJlMCN/wWJmEnMThj01oz/8HmHqSy1C8Xnv2nkeHB2q5boSEX7n9tutZzT+CbMC2woNyw43AypzG01uaeEWSh0sldqT/FFT7HItLnN5/sxDby4ws3/+X5ukIG6x07NMTkKBgu9pujusd2uIIBTzr4TtqDAIoj5L/81usPjC+yPrZ6BqtdjHQj00WB66maxuzdpr2LURBzdggU7/6dRUoibhHrHu27B3o2Nx4NpnulOOXMgFyxfruOZowQAAACxBnvtFNEzf1+r+1FYhZh13lr8fS3v18bH9CE4mbV7h417LGGti+gpJ8stneQAAADEBnxp0RX+NmIKr+C9nWHyDrxwMHXm6plPWpdYz9mPCwis05VbBSSZGa7pefK68Un2SAAAAYAGfHGpFf9E0GDu+ktvFMGyuyP+/q8a08x7N3S8hze5kbadJPQ4J/VcgfzmVJ+wGbfISiHM3iOG4kNfnUv4V+1AHLRdp1XpMOeQfEcujnC8EllvBq6VYdpBNSXI+IGkPIgAAAE1BmwBJqEFomUwI3/kqHHwSjggzj87YkHr4a87jSRladhRJ0sPf1vzUl8LaQuieyIjiu6qDqBhKWXMid6Xg8mRn7LGXUL7awd9GqdtR0QAAADBBnz5FESzfg/tuGYHKjTB5ZIF5DlRZ/mG18mLNwoqUKDqUjlTWk6UxETTkhiQI1o0AAAAmAZ9fakV/hwRZbYwm8UV+fNHe+WvQ7LE1dhsl0Kkl2dgkJM2xQPQAAACVQZtESahBbJlMCN/HqjpQil7yyLFLFu10qfJCouDtF1Xmx2Qi5MZ8wiTfvWELZh8dDesXTIOc3puWHO2V2s4k4HdAKOR4ngqVZUA0LYM5DJacVR8U9Naj7I7kVfa6bltDE6u1WaZn72qoZRJsNFn5BmTYbmEO/mI8pJzVOUzeRtqmdX+yIakKz1bUKdoGm4/J27cXeasAAAAxQZ9iRRUs36xetRXUJJO0H33aYF1R/wgPQCJEkQv6Oc57u5yk1XK8xotcqZMjal7waAAAADIBn4F0RX+P9L+RFJf/ZGxnwlZxxRpnlAdZ4MTNBABowPPX1SEIZbQRP1HKfV32CEsflwAAACUBn4NqRX/Z96jvz6lPIdxMK5ZxEfdrk4JnwEbzrXk8OtLZKf2BAAAAN0GbhkmoQWyZTBRMb/vuVw4DvMhdaXt49Ohno7dC9VWUVXFssBv/tAEi1GxHEtwjRSRy8OIHuSYAAAAXAZ+lakV/hwRY0JTl1qnyRaLRk+dkPKEAAABqQZunSeEKUmUwI3/9Y3sZBuQxneh/rGW4lE9nuaCObjg+K6/Ef2UAcSMCaL0//e80Z5sMPf/FLcue0CI5qB5M2c2QWFN1gHozimkN8YvezsbDlRHLpDuEB8yJGwaUJGSNmiArYmFEwpDpPAAAAD1Bm8pJ4Q6JlMCN//1ikeX7HlErA1F/NBuaVPRthECIky6bnR/zbhvjqszzH8XsGKtqLqtEjwRPCnmhPsXXAAAAMEGf6EURPN+D+24ZgcqNMHlkgXkOV4kgZTIIkxduFFShDLukcqa0nSuPAX02qo7ZcQAAAB0BnglqRX+HBFltjCbxRX580d76sSrub4Ej3N0tuwAAAEtBmgxJqEFomUwU8b/8CAhiLA9lUJ1ZhKmJlcycBCqNW82vUrKjwfRLn3brEJAyFEiDnxP6vgG4feHLTjXsLOkn44268wtNImWMf5AAAAAtAZ4rakV/jYz8hJwlu0SinQC47vIeaSqHA97ANM9n5sjiQxvenF11Aous9wBFAAAAUkGaLknhClJlMFLG//vDoZtU5Qj04CcZokNlVJpbn+xpxn5vnomGzZJoOL36cb95LTn2GQWez/KCm44eCBSdizIJg/0bpdMf7OAdSKNj8pTKjsAAAAAoAZ5NakV/jHXt2iSnj8+pK5UPI/1KAk39osMlGVfJu+lmZ0egJ6vDwQAAADRBmk9J4Q6JlMCN//wDwzHAd5j8NJF8og0066g+rXTd5exDKM2MCnecCF4E6scY10T/iZu4AAAASEGacEnhDyZTAjf//WN7F6EssGLL9aRq9GMDYogP8Xzv0eGCplNOy5xCXRSUZ3whgiU8wFDYxIZ/nwDS8NdljQdsca56LkdmQQAAAHxBmpNJ4Q8mUwI3//vrhHYHuWOrKSbgXljf8SHjA7pt3GOVNXvZVrQnA9aKMhM1s43sy2s+zopKgZQuR3OXh19uu03zK43qCzT1h20dR/R17wlmMK6XGC4UZ+neex19jtPD/686wWjM+ofcRmWrGJuZFnTOmBpsxXPMq7oNAAAAHUGesUURPN+B+FaPuhgDkzcf9gPd1YURPpDElZWBAAAALQGe0mpFf42Yll2maqZeq3XThpLr/MMrxEP4wtMxgfA47nZIAB0Jg2xgA4T3wQAAADdBmtRJqEFomUwI3/vgOOCJW+nXGeBH0bjrWPxLGdQkrwjNTNgde2mMiCqnPmK8M/7dGPanLBlAAAAASEGa9knhClJlMFESxv/8A6IrIEOZqgng5VRuFA82zmJM8KL0rKYQRJ7Z23FpDej7QMQVdrodIfe0IE+0l4nF1TNAXWq6QYt1HQAAAC0BnxVqRX+NmJ8hS3aWVfP2c1jQ9YmStz/ocuUcfXzyBUtM57gT1Y3KU8wl/ZkAAABKQZsaSeEOiZTAjf/76z44DvMd9wsd+BXDr5TM0gGx6Y5mxrdaiR+xZhz38vjjy0wqFQLRtVm6z4tKSoPcbC90J+/nHLhkajPTm4AAAAA5QZ84RRU834HLEsIWvPinvfHxN11tVvEp3U0V5MHr+u9+xSzIWHqzyYWqp/rDE7G1eS8KR5LYfoqgAAAAJAGfV3RFf4kZCgkjcPXSmeg5eQB5cdFgpKJnWyRjJ0LC8/6RsQAAABgBn1lqRX+HBFjQlOXXoyp+tUCmnUXoUiEAAABUQZtbSahBaJlMCN/74VHYQpILVM82IUwsR3tP1BZNdPf7nkM/rWUZ7Vjytb4wtxQrFU0OUMs2AySifB/5qpsYFfAbi+7wg9W7xVeK6NYvm492GehPAAAASkGbfknhClJlMCN/++A44DvMfbctm/iNKou1bR3qaQPdbYPAwRr192Z0c7+EQI4zvYW462uiNmrbmQcLA9GkcsKw1co4BTWHqy2AAAAAMEGfnEU0TN+D+24e0TioUzxNily9Go1T0H+2EjDCXB2dGC0c6xM99Lg6Jduz8xAlJwAAACEBn71qRX+HBFltjCbzxryJ9mcJ8tdrhFmiT0jMMVk0HmEAAAD/QZuiSahBaJlMCN/BI78lscgq/Rbp6OxwvfwmtoGweCC3o0q532h4ICIwjwcptqf9EK/+79F9P8RvOlrjacSPiqIg7tu0F2k6OuQHzHZomSpzp/xnEFVc3YpD8D7mVeTAU9Xkf0URAjJ5lfHVioB1x0ui2g+17Y3BGxACMJ3vWNmNMsSB6yWO0rku1Z5c+jFLkY2xMy95ck9Xi4MpgR/rgU5kie+i3djqs+2ji7H6EQ+ogtPCUzbkhOsAieuOLwevqHshJvQds8aT+lB97TlMWAfZprX9Mrv3feABeyzfKAEPhJ3BSA1JjxJgX63hdba2srRrtpcoYk81dOBi6AEYAAAALUGfwEURLN/QU/leQXMu9VvVKf5/iBcuPswhbCCq/njMDoMNrlzDXkngFpZCXAAAAHEBn/90RX/WyxZvpLbxTBsrsj/wSTqMIPKPLDvNeh/1F4bmWrWmiWrQyZcVh27RFCrcMqxQPKxSxC/gK2atA4KwaYxGxim8cYsJdnYB2lEgpZEZ9RrWqfitTU8KyDfSoH8YyYLFXyK9azzCP++iehmJGQAAACUBn+FqRX+Ky9y9AWM9M+//Zr43fS7wb55u5yXHFaE5OBuT0DsMAAAAREGb40moQWyZTAjf87uziMWmfzdbE8J87clOeHoYJgH3Cx3Zbf5fu+tjc/rFA2s6Zalj2+4xEwd5fOTh9i8yAEFuj/ORAAAARUGaBUnhClJlMFFSxv/74VHYOCF6yTT6YYA389+T2IXKVeD0g4f5YiUfdwqkE9LXLSW0Wg9bBFbTjG9iud0FpzmEuq0RQQAAABcBniRqRX+OkjwIpFLTj3OvmKtI9JIoYAAAAJJBmilJ4Q6JlMCN/8epy8Hmt5nkvHwUaGRnirECn1F6kQRmAK+NpNIq1+wKlemlXjXEhlgMZ4GsP1pm9vvJhG7Qc9ADDBVLkvTUXiuLhFe47rYgESCog4vHJ3mr48vRHWscHlSyjaWuoADQyZ2fVR3rFtDDYeIJSS40gQKiB4RIui2G6WIFhug4v4SxqnhvpgyxgQAAADBBnkdFFTzf1+NzzhJSSLZXInxj28fVa9sZ3iOeP2ue1tve6YTVpohEK9SBnnnNjSIAAAAmAZ5mdEV/hn7CXSp3/tuQ/gVx//v4sxBQVVQo1NUSFtg3nNLgRrMAAAAgAZ5oakV/0LYnEaxS2mK1fAK4ZH+xIFCrT1VZklQbIOkAAAA2QZprSahBaJlMFPG//APDMcCEr16uk80rHrFFfOOZRse1TbxZVz1dCEySWEu+EN2piNYDrHViAAAAJwGeimpFf4cEPjJJVPrWo1K1z1x0dfojX58CSEdNp6MuCQeaYl5k4AAAACxBmo1J4QpSZTBSxv/74DjgQlevXVYpZHYgY5ILdPK/4Pa1MNf4OiS8dl1R5QAAAC4BnqxqRX+NmJVnz7XQ5t2DE0nG/9r+8NOf3WQ8rd79EUL5OVWw3o6JYHnJty2AAAAAa0Gar0nhDomUwUTG//vBQYpYQEgvj00Y4sfEOWfwyh/OqKfKANvwZZWb7+Zzvu2GXqL7tu9BEtBF1qEI4yW/avkwQegNVC2ROsxmqz3xMUT7hq6sPN5B7n/7jXSLByhN4LaYlMrIVP2Gme+pAAAAFwGezmpFf46SPAjbqplSkyFjtDCI/KdAAAAAUEGa0UnhDyZTBTxv++A44D22Hq7hOPzCa+Q/0uZ8jWnVjeMaXFL05CiAEmr94qQQKh64dWL5oOBuv5DC0B686Pn9ihyHP8aNBy14cSqPoOJZAAAAKAGe8GpFf4cEPpZCBxpCqtM230lpYDo4d21o37/cFMIRIifuqQPwQmMAAAA4QZrySeEPJlMCN//74DjgQ+vwaqsgahBQipjp6SW8umNWKvqDeH4JFmwRnU6qYAsliRyGwuOB45UAAABOQZsTSeEPJlMCN//7w+5hE7FgdvuOKx17H1EIu8P6dhdyNR3Bp44IixcplMPwMmRmTbkZKRLRSf3U7jRY95x56Yr6mgyfOofav4khhB6BAAAAV0GbNknhDyZTAjf/+8UQAxkZAR4MbGRlJS3hcrKcA7AnvEOlxAMEgQ++P4BRPl+kFk9FtV5R/aKjnv2HOToKh1xJET0cChc94nwhXYYLk8OsF9FeDw4hcgAAADhBn1RFETzfghKjLXhVTVpt3odhU1/9thHbD1IrQDd5C/7EbNj0Cg37PlRRDbIVjd2YJ6YmirkTIAAAAC0Bn3VqRX+KzvglsL6ygpfSqzjv723KBBWB5nY6qSoX/1oYogCyBnZuxHtpF3EAAABDQZt3SahBaJlMCN/KCEDzIZX/gifClN7W6rOMfzBCSLAvaxS3ajNlnvJx6yl9zKnNgrw9/FvayjPOvsK8P/j0jTsPuAAAAClBm5hJ4QpSZTAjf/vgHIEFoP7SF8/0hA40oETmSgI6eYrw0ApjXdIPoAAAAGJBm7lJ4Q6JlMCN//vBGT19kCHNXXJVpZIVW2dOsxg+4nBxXYKH8lgiSB4xOvJRCAiVFRsPHQ0NMqcJypOPU3YoZF/tZgsowrc3AiXB3rAlfqaAL7OjHoF9SqtbiXfhpU4xMQAAAGJBm91J4Q8mUwI3//121hr05LoQDvslraTvUwVhvuZbxxeVmAmt8i59vy9EIHQrpGFHmbLnzjIT69y4Yxu/aorB5LH0qX9SvpgCyK/xkpHH537XWLW78sVsfAh6XcctCpaW8QAAADxBn/tFETzfgfhy3ZPoRbD4wmzsftxxUTshAaOeG2I0yWL50v57GYufV/HImcKo1kE6CdyqZME4g3D6gpEAAAAmAZ4adEV/jZiDOg/2dUiFLHxw+J/iID4obTfYetWmDphQQWMG2L8AAAAgAZ4cakV/isvpPCgm6QyWAb6SK+HCMEJXEyPb987umxgAAABPQZofSahBaJlMFPG/yghDLjqnQ+U5Iog63cQgD0Zbv41jiJ1Bb0LhUXR3RIPLhvolGP5fCuJk+gAJDaNEK0tSdXgn/kDyA9nx+m/zdv65gQAAACgBnj5qRX+NmHWKX61xnqOPLP7m1eVao9/Jf8Igt0BHxeMZyj2uGarsAAAAQ0GaIUnhClJlMFLG//vgOOBCV69dVilkdh/3zGtCpgFkyYUdvr7GH5COYLGX7YS8knmvd86QQESRdt876rG5nJMdX+4AAAAwAZ5AakV/jY0SA6wrwXjljlqnIHhE5NeeEAmpe3+gSytfIFQvKbbmEuo179PCD/OBAAABDUGaQ0nhDomUwUTG/85HJkVD4qSGOwcA3K5VwP1c6xtTR4VzmQ0nBu1gOtLRFBdIfSP3an9P1gmsInCPe79j4zuUsL7DK//x4TUhKV8qs9aaEd6tCVykWUn5Gopj46/K++Ykslr+v5UodumlbuShmmcRvZfdNrYnmbVICpyTGYIOI3oAkq6vllklL4brVgHvyURSStEFd3HHzSm7Ib3PtR4E5I89jUSkUoRqP6CDkJUiXvc83P9RW253rviuhvzqjem5Q+hPFgGTABoRk2ZLNhrce5zzCxaYC6gToCoWrl4lzpLNEOsFnUNIz4XxjMda/67iz7TaE3NHffWa7Y/p9uDhqQF1fsC6zz4vKXmUAAAAWQGeYmpFf9Ei2Luv/RNJcaZzW23Slv1beS/YB+iADhqJnO8XGwim1UkZ/5DCTycTZV+kD7aTYfAV6mID48v5zZgCA1mNEtUUWkqJoLo+Ei7fqexybID5ptuhAAAAZUGaZknhDyZTAjf/++A44Hc4/O2JB6+GsntZ/QVS7FVbo5y6gb6CO1Sl0lysiHpH6tBqj4+RV0j1AkhNZawWUW7mw1Ydkn1vMjA/KVOpoHVQwnkQTNeO2ZHz8y7tU10XuEBdXu1VAAAALUGehEURPN99TaxHbgQG60QG5CYHG0EwwVTLS+WEIzdD/zb2JngNyA/ValMZgAAAACIBnqVqRX+HBFltjCbzxqFwcAaE+pOC4nnIXAWqYK0KnVEZAAAAWkGaqkmoQWiZTAjf+8SBZr6iDxTgWdHsgrtUrFBwt9rOo3XBJP8ewDzX8GtwlWBgrC6irWNVrTLY+WKlSir9pSwV4EguL0xV3Wcj0/zw/APuEjV4PAH1Af4Z4AAAAC5BnshFESzfgh5hlu/y+9zSPV3/9spsZbsjm+DaIPLk3e+H8QEI6F/J3CJy6zchAAAALAGe53RFf42NDc3tk/BfLpHtYrknWRGdfCfWGF2hHwQDNNRoM7TAEbExLqzBAAAAIQGe6WpFf4x17dokp4/JyHzCbIpQYkeLYHCEqpiCFaz+wAAAADJBmutJqEFsmUwI3/vD+CADeEGcfiMMXyiDTP3ydcyGMsECu6OwPlhc6nEUtvJobaLBYAAAAKdBmw1J4QpSZTBRUsb/x6w8INv7ObYOML18RRf9FGE1XcGr6KXtfZJPIpBQ3Zddf/xcPS72YLTyF1aF1OgpE73KGCe4EKRaaP6+28u2fFYqgdEy089G2c5o40yj0KCcaF2ckf3qEd7wZbN7tzArbJ3ihmBK1aiIZDAZIELsAMy+cnsNyfdeXk6vZoOzvGLtwNlOVFAWfFH7l4z8o6rut3fiMJEBlecPgQAAABgBnyxqRX/QxMwwr1BpDDFywfg+dwOr6eIAAABJQZsxSeEOiZTAjf/74DjgIHS2PuRVzH01Z0BzuHiEMxkS+es2RQpgPq4cAkl78+syHU05veIX5apNghHwp2dHWeQBuTkHc9KPKQAAADRBn09FFTzfgfhxltJIut7cibOx//YvRw99xcIjnvocl0u27U4ZQtRxhVb+2ZI+NBhFvmHQAAAAJAGfbnRFf4Z+u/AqLSLb8iSXq//onmci1775jnjU+EnU1sAD/wAAAB0Bn3BqRX+Ky+lRxhN9YRwoFckJ8K7wgeXkfQ8c+QAAAE9Bm3JJqEFomUwI3/wDoisgI4JCqEuNC9+tPU5T6akQg8IizB7OrAsSlrnkH+zL9hIrZLMG+0x3pM9DfD7xIYuRzu7XLhI5qDban3u9P/d5AAAAVUGblEnhClJlMFESxv/7w+5IrxwG7Vm0o9WyHVPRTEK7sPH3Tlc6BafENz9DO3jdvcZDYYjxJ/xezZD/wVOgNlWr1ovR60fW2n2QPDIrXRl7wbJGuBkAAAAnAZ+zakV/jZWjbgdMtmBO6kL9v/+iv11IkCbeIE0lsf2Q4YI/jwZ1AAAAREGbtUnhDomUwI3/+8OipWXVEHfdmUcs3voSdMpPrWh0B7x/buw/7bfWTU2SnRyzDOlwhY0AHOKdTjMVTnbawMzXUYswAAAALkGb1knhDyZTAjf/++AcgTTZH1Q4wq20ZHYluqF2d5yizb76mgyembcA7BYepeMAAABaQZv3SeEPJlMCN//74VHYNYZ3xEb5VWTtwzMtJdYmcfzq/85J9tfpgIKEqieYPeY/MEIfk5XhJ1koykG1DVrGfyuRE/hx6dLKPysfIvtARn9KJW3Q6CD7moIQAAAAY0GaGUnhDyZTBRE8b/vgOOB3OPztiPZIA+2600lWyYtu5etLlp5Z1h7gvaM9dZAwSh88F2MaIP+Gb/x+JzyfKCfhrWJAkMuh2vjzRb7d/N7ALJMAooNT/8nzsWXFsQPjIw4BwAAAACsBnjhqRX+OeGijP1BCTddBMbj/ecV0Or4a5xYPj/vYTAUTsVBHuE5kBqvhAAAAOkGaOknhDyZTAjf/++Acgl11jEgudcSnSrhTGl+N28Vhdn1HYhWUAuZUXRLf5VeB/cE3pIjWL90Bv0kAAABeQZpcSeEPJlMFETxv+8PaaJ1sN1uxRNIGqNtU2k3406usYCk1bEtXHnFK6NYx2++uiXE5dDpIBIRXDOXY8QkTWaxAEokog4eXK891HbBLLwiFuVsjJhcQDmLu/1BPQQAAAC8BnntqRX+NjQ3Qw5SeeQtnahQCGRD+tBtlepn/thplttEshgUAcjhCMocOSDNHoAAAAE1Bmn9J4Q8mUwI3//vDpMA3iJWBxGCoVqH005LOHvO3JnF0uPAF/1BdrAmcA6ADeTiMuCgJLUTG5+vRnVljzqS04Al1sJB4spczTDLw8AAAAB9Bnp1FETzffU2jm3iDcWTT8O+Q1NZfwE4BDhgPUoUdAAAAMAGevmpFf42YlWfPtdDm3YMTsd9f+YkkWkcv8aPIfR7QyuqOLTk5HzNqkb26HGVg0AAAAGdBmqFJqEFomUwU8b/74VHYnLGfY5FAo39Th64FzFtgZ97sVu925ubsyqHBRHBm16zii8aJWj58APV75EudDjr7xLleCLLq7/RtBuOXcv77e28R66Sg0C5+ohD0irjcvKrApu8KWKOAAAAAGAGewGpFf46SPAinGc0bJk9Hz2z1Me3AgQAAAQNBmsVJ4QpSZTAjf8fMy8FXp/MBuvzFgdo0Sga+yREhRElJ1uA9sIsY4ra6c6fc00xs5DNAd8Y8yfothafu7uajsv9X2+oe8VfvZheOdZXtqkEIPdjDT49hK59jghSaTBGRE3a2q4B8vzpyVLIVGWiucmJcTr7iO+Rs1GKykq2Hqpg/7wGE9uYxrJNr28ZDTVBHR/4CyCAPQk6jWNczPh78rCn0z6G9a1RjzePKZYANdrkvuc+8zCzahQHbPyJ3wSnp4GBi0FZoLY5fHqWlr8b307+UI2CHj0yzFgHzBe7+BMVC9y83a86LgcLsFvxDSFgPEYch+RqKUMFJwfV/KNr0okJZAAAAQUGe40U0TN/X6axmThJSSLrDjCbOx+2e33lIIgsTG2JtszOBl/PYEZ41qb1VwqSpMkCnW2Br5kIl2A3Uu0IPihiHAAAAJQGfAnRFf42Ygzli/toiEQB9cbIH+nvDxV+o1J32C6oS5Fi80REAAAAoAZ8EakV/3mnpOLtzNha7JYFMdaGbfLAP/sxzAhGKBTfBtRpxTI6dgAAAAIFBmwdJqEFomUwU8b/0EQS/wgjd+gtxcvUsjBZ/KvLmmxO81sipaPVWNzgoVqy31nJce7cJ78sV7DeSUW+b67IBOE8jk8HFII54ZV3rWpp/dqQpuWbfoc2fWtU2v+/yK+ia43/m1sEOBWkif02dhVPEQ9NMhk2dwH5CcI/zvbk/eCkAAAAoAZ8makV/rN+DaXdiJ3Sfbla5z0prYIJJdn9L5fmjpAFnOrwj7T71gAAAAEdBmylJ4QpSZTBSxv/74DjhLmEQjvo7MOz/6KwI8CxsOXVk34E9cwzHoe+ypu4pyoHVVLROmPuXefirSAYX2k1/KeHKYNsg4QAAAC0Bn0hqRX+NmJVnz7XQ5t2DE0nG/A/2tAvwsk191IaIPxh05qExET5GLoDSl9kAAACTQZtLSeEOiZTBRMb//WN7GRlg7p5tdxIOcz4ktWOw25b+IeCEiFor+loK1sW6QJ67CqjJiEE34iTVIck02sn81tu+PGvjsyHqi2o+NT4inWWVtqWkpr7f/3c+vKVzlj1mjdhK8OP4T7BUoxX0iIL7Ms5LBKBxFY04P++ZvxW8cml6mweqdCiwy+yuvk365ebqBsgsAAAAGAGfampFf5XYaTriDUHbSM4/2rI012dTgAAAADxBm25J4Q8mUwI3//vgPUQ9+HnYzR0ReHsw8528FstVpSX9I30YzTO1HQX7vIeLO4umR1DvPGSkhtbazhcAAAAqQZ+MRRE8331S+se7kbW6AxPsrNhK/dtL9AZtyTBgH5jIFFvIWlbz+Y1vAAAAHQGfrWpFf4cEWNYpbR3VxAdD0L/IP/FPtuyuyqOpAAAAgUGbskmoQWiZTAjfx9CcA6khDjKpbsCmICblPVjONIzX1mXC3smr+Dq5fXiG9Xv8kFQyUglCZwie8jt9HTy9u/pPWV6Eg7yLHBH4vZV7UNGZW/41bil6995IKcn441hEAdqxiSHI4vuUl9rENOzJKWLV2U30qhtdcyYWhWRdSq79ngAAADFBn9BFESzf0IJ5kmO08++CFl4DaPf738KLIygT84teS4UEhuc0uV5bKc3qgHgZU8MlAAAALwGf73RFf7OB2xJd3tC/Pqn8/HmIdT3BpECF6yr/lbOPB+Zy1K11SUfd4VAGF2JZAAAAIQGf8WpFf4x17dokofjXiSEcvI/1LMfeM2Env+wlhWYh/wAAAEBBm/RJqEFsmUwUTG/9YpGqI6+gv8B9xK7sh6JffgqkidB+uyLMXamYT/v0gM2Bl0cBW+zOs7+meEYWSU7mTdahAAAAFwGeE2pFf4cEWNCU6Hlhkpwoe3NC1OR+AAAAfEGaFUnhClJlMCN/x85ZyjCkCh8uzK70/iv22/5Y3C+WTfpvUCrA+KzvU4pHDm4sP40BAbPg567zdr1Wu7EEBUuR9mVRdZyCPSUp6JfGVxbeoCt4oVKZm8KmBNWkYt6ZDzaPhh8MV+sn/5rn/+5/sTVujqfTRrIRFR+q1bAAAABYQZo3SeEOiZTBTRMb//vgOOB3OPztiPYLGqbk5d2BqIQU3KblqkAke1WFGNwX7uSXHgcpobdidZRPvUHXnKBlNvG1whZJdIUyHR2g5/kj6iGYMgjOKwfAwQAAACgBnlZqRX+NmJZdpmrtcZ0NTAN84Rf1N4fqx73Qed7w+gCCXwdxAjG4AAAANEGaWEnhDyZTAjf/++A44Ilb6dVP1x6RsYOzc76QVwcX2znv5faz7QEaAFkrcI7CaujLN3AAAABVQZp5SeEPJlMCN//7w+5d2nwAIi2iZMsWHBHowYX4aPQ815YaFI7qF/qVwMK0/Txt1GjsuoBrg0JYfv/uwERjL/ROpxdPOR5x3xoTlVPE5qTT470ToQAAAFlBmpxJ4Q8mUwI3//vG3LzDXKIqZ9TYmttr5CBr315a/0BUuW50ROufAOdVNE+12YSjQ+/9Zk6dVP/79hzk6CodcSRE5KWMo3xZZ6kBcB3u5L6hIx3KSQR9gQAAADlBnrpFETzfgcr6UK161qa1NGdzHnS/SFXblAfYkkTUgGALg0oSw2I9ZdDSKhlEZD82KSrfFpA+v6EAAAAnAZ7bakV/jZWjblZD8+kx6s9vusQNFqTkiIzV2G+Y2RxdmUUlRu/AAAAAVUGa3UmoQWiZTAjf+8OnUzwggj/qGfOg+4iB+MF4Y5ta94QwdwgcgpS5V9IyYNFhVGlpN2bjMQ4DNhuvd/qEmbi/FxB9D9m8UL/JpA3iUwbQMpYlEjoAAABmQZr/SeEKUmUwURLG//vB2iYdh5ImpKHSwtHernzZCXrV5CEdMKE6oB+B0fwylF2lU+/yPss1M1i+5F7zxa3LLlnpb90pQhlGEO/tirOcGbuioWk+zu7MXqDCxg3B7BB3iJfYGJshAAAAFwGfHmpFf46SPAi9dvB8N1WaHbwo5+8wAAAAaUGbA0nhDomUwI3/+8Gbu08WIicdkBLuDqjcxTRGWFEHK1YMItB/Yxti7mXMwKkgf/cswQBjw/L2b1agYYBo9t6xn6Eetw4bxPxz38vjh5Ug36pxN1P+Jdu6y5/v+SSnY/wBOAf2anagvwAAAD1BnyFFFTzfgcrcmZwgJIut+RPjHv9yMW/NWZe0k8Bi3Zgre6X89gTtwG/OmXBXHUG6b/SiwXTP+8AECry3AAAAKAGfQHRFf42Ygzli/0aMsY2QSz83/bIH9oh1NSd2YfdN+fUQOgXqQV4AAAAgAZ9CakV/isvpBhS2mIvgYfwGKq3zW41GlvjW/evQpscAAABJQZtFSahBaJlMFPG//APDMcJc0yddeUYH6nOgY2NbwtKavnsY+nBe+fxVlILL0NiRXrOhPUiA969gXTMpBWKdBefNzN3yeWc1gQAAADABn2RqRX+NmH8N/4LEDrqsxxCUlA+/C6d/hrlaE3Eni4AjiTNfEyqi1KVhrZEtHyoAAAA8QZtnSeEKUmUwUsb/++A44S5pk6Zc8mpbajeY3FQlOl5eERU4NlZQ7KCHXnQqL1aUm+ZXyoGSi1CXUAu7AAAALQGfhmpFf48rV2va6HNuwYm2A5WJIWicynQ6BVYFp9FWnKrYE2sDGYjSbKzVRAAAASVBm4lJ4Q6JlMFExv/HSJgSC1nZxJ/p92h7iaNLjO1oWGnVb7mdLzqjQegiZZ8BFVoHleRS9opigDSoevdqf/HRbxPUT937O3gQ14DzaDIrquleqdGqhoMap2XYPTsxa/wSkDdVT0ssgNi0XqyHVXOCMqt31kydwNB3y5PU4UE0NqfePmpADjVY3oiJGyZaYPsi/X4ym21aC/XEA8g6cHPxA5l4ZzRa4mnIZk+87eHMPyVrzjVuzsYf3L5Mdth1vbIMZuHrbzmxDF4Id95FDUpJLeBKHbiVKXlKbVlORXdE3XDZHj4OQ44bzKNetjD2jssvKy2arGt8LqXFFRWGchvabmDpRlO+TJnoh6Qi34c/JAelcxWZTHtnFBN5/wh1aZMwTQYfgQAAAF4Bn6hqRX/RIti7sAopo312LMs22rFhBmyBC8ZUUUusB76jdtD7pxWchSlEaZcpo+z3LSOsqF+TjWIbKyoOjcJ0BvP5Un6zNu7CYyOjMKakeOmtBeHghnZ0Lt7ZKgmBAAAAZUGbrUnhDyZTAi//87FwbhKNgmMp3eNkrIgo+BdeIZ2RH7aVgRWSRB8VGoJF1H932TYjOcWyo9X8eYosRzKDsLYZ8OElkDGMElhddcoxE4Cuo16C400E76DzMxmRGQrfcMoXwPeAAAAANUGfy0URPN+DBf3EVFfJcIvLYYm40FJuHP41gUVpmbF9867RDcpbG+hUwmLp76j9ncDp8t5SAAAAIwGf6nRFf4Z+tIoBTm+ZzUi4P/L4NbInHV4wNqEejzvRIyMbAAAAHAGf7GpFf4rL6VHGE3tN/0VO4T4Y92cJmTGqjpgAAABFQZvvSahBaJlMFPF/+G0p3IyKrpYoACoREvabxZ2Sa1j/PX4Ig63AFh5hVcDy6lyqDJVazyv9k0A3UEbpQ4kElhDfQ/kDAAAAJQGeDmpFf42Yfwkkpl9ce7oX5djjxlYebpvmnM3if/h60uGBIJAAAAA0QZoRSeEKUmUwUsX/+99LcS41uOHpiqjPsmwGusCKQ6BF1AYtz/WtQ7b6AecL2sZs2DeFgQAAACoBnjBqRX+PK1dr2uhzbsGJtgOViSFonOVO2XvUQkyMOnNQmIoMiU2QLcEAAABnQZozSeEOiZTBRMX/++B7/JR3TzHmkys6OPVr3j60/Ducpwbn9LZ8EMRIp1AAs7+bDOf0XhisAXR+MHkgbVlZgREbbfkD3LjS6EKrXc0GPULfC2RHIyE230IZnBmWLkBNNdGdzIhkLQAAABcBnlJqRX+OkjwI26tNPqSi/HkmF/g/gQAAAHJBmlZJ4Q8mUwIn//Dz42spRFihMPfTkj2TNj0lStYXk3a+C5Wtkfypzo0uM/feYzEOqkQzy1NzoYXThBgSRvjrEcejioh4gzCm8rCbzMI2wWFHN/yLtF3p6q9/Ec94jMIwPBQYnzCY283aqNgEwQc+asIAAAAwQZ50RRE836foBRLD06goiSPhmYv9qz7O6uD4czg9Q3XDksNdfK5w0IuoG1k3GS0sAAAAHAGelWpFf4cEWNYpbQS22jK73/5IyrwB5npkP/EAAABQQZqYSahBaJlMFPE/+81+QTCAevbatPJvZfPu+5FInLiQmsx/vsDDnlk+n51NMYohIZisWJ/S3zrGQtmid1/VGv24Rk3j0Dp9U4U6eejQnKAAAAAuAZ63akV/jZifIUt2huke1h+nmbVlvSl8BsecDgLV4KKiXsuB44t0CtoWXXNbfgAAACtBmrlJ4QpSZTAivyCC9u0SU8fn1JmlqS4H0O/V2QQ8C23N8Hq2eJg+n3lBAAAEpWWIggAb/6zQYZLfQPgNc0iYMN3sPl0EDBzzvrz1O9Sgfa49FGnVhJ8qLkhVezFoQxJKbc0nUGNZR098qd7OS+y89EcOWVfDbC64tS8Y9KVBO5bX3i8BNZhBqqdfBpIeL6SUDjC2QpVPdT+4GdjwHm+HVEddJzn4mxN+ZHi1zAImnjZYMgtVcr/8KsM8y6i5AmAGJNcUB7ayE0bca3a3R6hdrr0XJr+AAAXHQ/kXcVlmP6pUrkjcKn/5MsrYsfvEq74eZ4HZz2CqbE+TyZ4bBsWkF/G8EsnWTdMeCaT0oSOggGj6F6GH2koqwzCw8EE/jxjH/fgpvYdskan80JjRYRAJC3SwAtTkEnddIDJhOwFLkmhXRf1WZtt88VL7WgZg+Do7We/RCfv4cfPN57fJ/eEcGoPIdjySktCXmVnqHKZZhx61mqXrYt2ccmalI5pFwxn7yIk4NanyDkRuoRQdzVy7V/FgNDX+/OG313omVQVM1D6TfnNy1isPsCV96//sVHChbSvRvo9Op4uXUT2Vnp2NbBUnPOlIQwYUmnls4/qAf1DSyr347ve+HuWjGSfL2uAJLxNkKGL7jaqU87H350pTIU4+n/FRrP8UOsWWuZ9oqEO+lS3qvWMNvTA00dfS9Og3CEQ1lhvXgqkfbgmWS7Q7BrqjPYxv+usZZhR4mTsySOLjIGUswJByOkD9sXOn034oNMHPSMTs90mvpwuhhZCES9QIA0MY4mVJAH4U1175JsR0JGrNT2cG+kevDwhB7L2Ps9sq35Ja1G1YIr1fCqBH1NhFZbh3r/JJ6IfmaKkwRe/x35k1btKb+ZRfDYCZwD/U+DVYV4ve4/ux5EMulf81TrZ2iS/AeNl9oIVL7FLsYt0SbM4Gdwujxy/cVa/9DfeeCmTLktfb85Jq/PPzoWfkT+3MSEL52ahvl5dyZ3P9Gjqc/3V3E3UcMDLHXaFjLji1XATHryNndFbY++diGv2rJsu8jbVZYN/lqAhbLHtMl/sxkHV0L1mnPx14QjgEVCWYb8PE3T+6bY0rrXT6WTEzJq/uaxvgC3UliUMREWCB/LHkW4/FwMNKpTt+KgL7DKMzlTFtOl01XRpLhfNYIv4yqp/8/0Ljp/cAHvBSTd0o2zGUJLgBlXpkC7hkWNvVKuNH3gZDe7aBWygB4hHI5MtXigV2BDYnOAj1gC8+xGfGlAw3JTy1PkTj3bbr+p6m1jzipdInXtuw8i53LkL/+t/cDhYKza5lgkrWZvnpedOPwE+SDsrxeadOx3w6iN4kI3PIREK2JArXFqTIikdsxGeR30xC/R4MnKZUvO9n1HHNmqCdKQY/t8N97D5MniGESaR7uA4AEebTLOvxLuJghYdjoSxajwIKjHQQZ/HnrodJI5KNoj6gM3UJuupZ/7uHrjf0Dx0Tsc7GUns7O78xmwOt1zDx4hBNRbDjREElJr9eJlRZ3FMTZgL28DJXX2Ukq+M+ZYpT8CiXX8w7XSQxcMg4cJ6c0AGluEsCz3tAkAQZLU/VxMQpKTJR8+ULaCRGp3sYhJhT5mx6NspTrLO4jl7T5GLVFULkYWq1jSnmblAgEE58LcEAAAA9QZohbE//55dAJChmGePT0n0Li66IQjlgCcvSeXFOTWduetNIn5ncKj2uvzaGAWdM4YtqdWioqkA0TyHz0QAAADBBmkI8IZMphP/nR8JCz0aek9id9vrF4lLN2RgQjyZr432P7Kp14LSDqmMhMkWW0IAAAABgQZpjSeEPJlMCf+dJU6LZgkV02QLvEfeXB5dv4nCjjykgCXjsebUglI9rrNk3mZJmTaQFGdOlZR2+jF2hfcbd4HXQBsGGhxrxbbB//345uw0C7pMaLkJqkj5rLI3WN+aIAAAAUEGahUnhDyZTBRE8/+ZYKy4kVT7wb4JNhryfYT+xBrDfh8zeOyqxLHImSnk1hEFx+vGFOK6nSbea/LiCsJT6jX4eofbWT/4UXXlaQ7ns7HmfAAAAKgGepGpFf42Yll7P81LVQddgA183/eHh4rICR6Rm/TAP+50pi9D2H4nE8AAAADBBmqZJ4Q8mUwJfi8K/Q8CzpoXb9z2cRUhHfcB/4ijSNB3DB8kDQxNhedtHQQegeFAAAABHQZrISeEPJlMFETy/jBNpyKcpBz2UWq9NhKZ8OdqT4vlDJSja2L4KsxrGpSrA2yYsC6BONsZSjEWJ7nESRHswZooLCvZWL7cAAAAtAZ7nakV/jY0N0M9lu0TcpcLEt4G2uP1I1ad/p8M0BAfmcyRE+XWn8kbnbCP/AAAAQ0Ga7EnhDyZTA/8JnI6fa/XOcPoz6Eunvm3DSqWZut6xANl//x1bLCAKYLkthnFxJePmxk5tfDOwQ/OimiJzESuj2PEAAAAyQZ8KRRE834HLEsIWvPiTtw1hFA7vKrrccI3560FD1CXoViI/ZgtgMQkFL76i8/+MS6kAAAAdAZ8pdEV/iRfHjugOkdFKyjh/gRLx8g9LelE/H6AAAAAYAZ8rakV/hwRYbMohViYe1/b50qjneEmNAAAAWEGbLUmoQWiZTAr/DOcGwr30MLcyf4RisZLdUWnlUYvXJVKV7AncbyAoLODntBs0+K4WaaoVpZfqfI2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       "ok": true,
       "headers": [
        [
         "content-type",
         "video/mp4"
        ]
       ],
       "status": 200.0,
       "status_text": ""
      }
     },
     "base_uri": "https://localhost:8080/",
     "height": 501.0
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <video width=\"640\" height=\"480\" controls>\n",
       "      <source src=\"/nbextensions/vid.mp4\" type=\"video/mp4\">\n",
       "    </video>\n",
       "  "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 31,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "play_video('mf_pong/0.avi')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NQmZEVKGF4Hh",
    "colab_type": "text"
   },
   "source": [
    "# Model-based training\n",
    "\n",
    "The `rl` package offers many more features, including model-based training. For instructions on how to use them, go to our [README](https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/rl/README.md)."
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "hello_t2t-rl.ipynb",
   "version": "0.3.2",
   "provenance": [
    {
     "file_id": "1nQvfx1EzY3ElJUy-FVF1G16okSbkeUa2",
     "timestamp": 1.553274233669E12
    }
   ],
   "collapsed_sections": []
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3"
  },
  "accelerator": "GPU"
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
